Interestingness recommendations in a computing advice facility

ABSTRACT

The present disclosure provides a recommendation to a user through a computer-based advice facility, comprising collecting topical information, wherein the collected topical information includes an interestingness aspect; filtering the collected topical information based on the interestingness aspect; determining an interestingness rating from the collected topical information, wherein the determining is through the computer-based advice facility; and providing a user with the recommendation related to the topical information based on the interestingness rating.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of the following provisionalapplications, each of which is hereby incorporated by reference in theirentirety: U.S. Provisional App. No. 61/477,276 filed on Apr. 20, 2011,U.S. Provisional App. No. 61/430,318 filed on Jan. 6, 2011, and U.S.Provisional App. No. 61/438,684 filed on Feb. 2, 2011.

This application is a continuation-in-part of the following U.S. patentapplications, each of which is hereby incorporated by reference in itsentirety: U.S. patent application Ser. No. 12/813,715 filed on Jun. 11,2010, and U.S. patent application Ser. No. 12/813,738 filed on Jun. 11,2010, each of which claim the benefit of the following provisionalapplications, each of which is hereby incorporated by reference theirentirety: U.S. Provisional App. No. U.S. 61/233,326 filed Aug. 12, 2009and U.S. Provisional App. No. U.S. 61/300,511 filed Feb. 2, 2010.

The Ser. Nos. 12/813,715 and 12/813,738 applications arecontinuation-in-part of U.S. application Ser. No. 12/483,768 filed Jun.12, 2009, which claims the benefit of U.S. Provisional Appl. No. U.S.61/097,394 filed Sep. 16, 2008, each of which is hereby incorporated byreference in its entirety.

The Ser. No. 12/483,768 application is a continuation-in-part of thefollowing U.S. patent applications, each of which is hereby incorporatedby reference in its entirety: U.S. application Ser. No. 12/262,862 filedOct. 31, 2008 which claims the benefit of following applications, U.S.Provisional Appl. No. 60/984,948 filed Nov. 2, 2007 and U.S. ProvisionalAppl. No. 61/060,226 filed Jun. 10, 2008.

This application is also related to the following U.S. patents, whichare continuations of U.S. application Ser. No. 12/483,768 filed Jun. 12,2009: U.S. application Ser. No. 12/503,263 filed Jul. 15, 2009 and U.S.application Ser. No. 12/503,334 filed Jul. 15, 2009, each of which isincorporated by reference in its entirety.

BACKGROUND

1. Field

The present invention is related to collective knowledge systems, andmore specifically to providing natural language computer-based topicaladvice based on machine learning through user interaction.

2. Description of the Related Art

Online searching for topical advice represents a significant use ofcomputer resources such as provided through the Internet. Computer usersmay currently employ a variety of search tools to search for advice onspecific topics, but to do so may require expertise in the use of searchengines, and may produce voluminous search results that take time tosift through, interpret, and compare. People may be accustomed to askingother people for advice in spoken natural language, and therefore it maybe useful to have a computer-based advice tool that mimics more closelyhow people interact with each other. In addition, advice on topics maychange in time, and any static database of advice may fall quickly outof date. Therefore, a need exists for improved topical advice searchcapabilities adapted for use with natural language, and that providesfor continuous content refinement.

SUMMARY

The present disclosure provides a recommendation to a user through acomputer-based advice facility, comprising collecting topicalinformation, wherein the collected topical information includes anaspect related to the extent to which a topic is interesting, orinterestingness aspect; filtering the collected topical informationbased on the interestingness aspect; determining an interestingnessrating from the collected topical information, wherein the determiningis through the computer-based advice facility; and providing a user withthe recommendation related to the topical information based on theinterestingness rating.

These and other systems, methods, objects, features, and advantages ofthe present invention will be apparent to those skilled in the art fromthe following detailed description of the preferred embodiment and thedrawings. All documents mentioned herein are hereby incorporated intheir entirety by reference.

BRIEF DESCRIPTION OF THE FIGURES

The invention and the following detailed description of certainembodiments thereof may be understood by reference to the followingfigures:

FIG. 1 depicts a list of topics in the system from which users may getdecisions.

FIG. 2 depicts an example question that the system may ask a user.

FIG. 3 depicts an example picture question that the system may ask auser.

FIG. 4 depicts an example of the type of information the system may showthe user when making a particular decision.

FIG. 5 depicts an example of top lists for cameras.

FIG. 6 depicts a second example of a top list for cameras.

FIG. 7 depicts an embodiment of a user home page.

FIGS. 8 and 8A depict an embodiment of a user's remembered answers.

FIG. 9 depicts choices that a user may contribute expertise.

FIG. 10 depicts an example of a user question.

FIGS. 11 and 11A depict an embodiment of an answer format.

FIG. 12 depicts an example list of all decisions in a topic.

FIG. 13 depicts an embodiment process flow for the present invention.

FIG. 14 depicts an embodiment process flow for the present invention.

FIG. 15 depicts an embodiment of a block diagram for the presentinvention.

FIG. 16 depicts an embodiment contributor/expert interface home page.

FIG. 17 depicts an embodiment objective question to a user looking forhelp in a decision.

FIG. 18 depicts an embodiment of a decision result showing a particularrecommended decision.

FIG. 19 depicts an embodiment interface for users to set associationsbetween attributes and decision results.

FIG. 20 depicts an embodiment illustrating how a user may edit adecision result.

FIG. 21 depicts an embodiment showing prior revisions to content andchanges between two prior revisions.

FIG. 22 depicts an embodiment showing a question being edited by a user.

FIG. 23 depicts an embodiment showing the revision history forattributes.

FIG. 24 depicts an embodiment of a workshop interface where newly addedareas of advice may be displayed.

FIG. 25 depicts an embodiment where the system is asking the user asubjective question in order to learn the preferences of the user.

FIG. 26 depicts an embodiment showing an activity feed of recentactivity by contributors.

FIG. 27 depicts an embodiment showing results based on multipledimensions.

FIG. 28 depicts an embodiment showing multiple question and answerresults in response to a user's unstructured input.

FIG. 29 depicts an embodiment showing an example question to the user,asking for their preference.

FIG. 30 depicts a similarity profile of the news personality Glenn Beckas determined in embodiments of the present invention.

FIG. 31 depicts a similarity profile of the personality Martha Stewardas determined in embodiments of the present invention.

FIG. 32 depicts an embodiment for using a third-party API to help learnabout a user.

FIG. 33 depicts an embodiment for using a third-party API to help learnabout a user and to target responses returned to the user from a userquery.

FIG. 34 depicts an embodiment for determining an unknown user'spreferences through the use of Internet social interactive graphicalrepresentations.

FIG. 35 depicts an embodiment for the improvement of user taste andpreference profiling.

FIG. 36 depicts an embodiment of a web-based advice facility interfacingwith a graph construct.

FIG. 37 depicts an embodiment of an interestingness recommendationprocess block diagram.

FIG. 38 depicts an embodiment of a local discovery application visualrepresentation of recommendations to a user.

FIG. 39 depicts an embodiment of a local discovery application visualrepresentation of linked detail for a recommendation to a user.

FIG. 40 depicts an embodiment of a local discovery application visualrepresentation of recommendations to a user.

FIG. 41 depicts an embodiment of a review of a restaurant.

FIG. 42 depicts an embodiment of an interestingness recommendationprocess flow diagram.

FIG. 43 depicts an embodiment of a geographically localizedrecommendation process flow diagram.

While the invention has been described in connection with certainpreferred embodiments, other embodiments would be understood by one ofordinary skill in the art and are encompassed herein.

All documents referenced herein are hereby incorporated by reference.

DETAILED DESCRIPTION

The present invention may ask the user 1314 questions 1320 and based onthe user's answers the system may offer a decision, such as arecommendation, a diagnosis, a conclusion, advice, and the like.Internally, the system may use machine learning to optimize whichquestions 1320 to ask and what decision 1310 to make at the end of theprocess. The system may learn through users giving feedback on theultimate decision, whether the decision 1310 was helpful or not. Helpfulsolutions may get reinforced and associated with the questions 1320 andanswers 1322 that were asked along the way. When a user 1314 says that adecision 1310 was helpful the system may remember which questions 1320it asked, what the answer 1322 to each question 1320 was, and mayassociate these questions 1320 and answers 1322 with the ultimatedecision. These associations may be the basis of the machine learningthat learns over time which question 1320 to ask the next time a user1314 comes to the system.

For example a user 1314 may try to get advice picking a bar to visit.The system may ask the question “How old are you?” and get the answer“in my 30s”. Ultimately, the system may show the user 1314 the decision“Kelley's Irish Bar”. Assume the user 1314 says this decision washelpful. The system will increase the association between the question“How old are you?”, the answer “in my 30s” and the decision “Kelley'sIrish Bar”. The next time a user 1314 comes to the site looking foradvice on a bar, the system will be more likely to ask the user 1314 the“How old are you?” question 1320 since in the past this question 1320was useful in helping the user. If the user 1314 answers the question1320 in the same way as the prior user 1314 (saying “in my 30s”) thenthe system will increase its belief that the ultimate decision is“Kelley's Irish Pub”.

The system may build a profile of each user's tastes, aestheticpreferences, etc. and learn via feedback which decisions 1310 are likedby which types of people. Alternatively, an expert user may specifywhich kinds of decisions 1310 are liked by which kinds of people.Learning user's taste profiles may happen through a separate processfrom the dialog of questions 1320 and answers 1322 asked by the systemin a specific topic. For example, a user 1314 may separately tell thesystem about their taste choices through a different question and answerdialog designed specifically to understand the user's aestheticpreferences.

A user 1314 may not want to spend the time to teach the system about allof their taste preferences, and so instead the system may learn, or anexpert may specify, which of all the taste questions 1320 are the mostimportant taste questions to ask in the context of the user 1314 makingone specific decision 1310. Out of the universe of all questions thesystem may know about finding out about taste profiles, for instance thesystem may have learned there are three specific questions 1310 that arebest for when the user 1314 is trying to find a sedan under $25,000.Alternately, there may be a completely different set of three tastequestions to ask a user 1314 who is interested in a SUV over $45,000.

A user 1314 may also only tell the system about their taste preferencesand not about any objective questions. In this case the system mayprovide a ranking of all the decisions 1310 in an area of advice basedpurely on taste. So instead of the user 1314 saying they want a $200point-and-shoot camera, effectively what the user 1314 would be doing issaying they want a camera that other urban 35 year old men who prefercomputers to sports want. Users 1314 may indicate this preference byusing a search interface and choosing an area of advice that isexplicitly labeled “cameras for urban men in their 30s” instead of the“which camera should I buy” area of advice. Alternatively, users 1314may indicate their interest in making a decision 1310 about cameras andthen opt to not answer any of the questions in the Q&A dialog from thesystem and thus the system will only have subjective information aboutthe user 1314 to use in recommending cameras to the user 1314.Alternatively, users 1314 may answer questions 1320 in the dialog thatare both objective and subjective and the system may then recommend acamera based on the combined objective data about the camera andsubjective data about the camera.

Users may also enter new questions, answers, and ultimate decisions. Thesystem may then try out the new questions 1320 with future users to seeif the questions 1320 turn out to be useful in helping those users. Forexample, a user 1314 of the bar recommendation service may contributethe question “Do you want a loud place or a quiet intimate setting?”.The system may decide to ask this question 1320 in a future use of thebar recommendation service and through the process outlined aboveobserve a correlation between the answers of this question 1320 andrecommendations that users find useful. On the other hand, a user 1314may contribute a question 1320 that has no value in helping users. Forexample, a user 1314 could contribute the question “Do you have a Canoncamera?”. The system may try this question 1320 out on future users andfail to notice any correlation between the answers to this question 1320and bar recommendations that users find helpful. In this case, thequestion 1320 may get asked less since it's not predictive of whetherone recommendation or another recommendation is helpful.

The system may keep asking questions 1320 until it feels it has a highconfidence in a few possible decisions. The system may also stop soonerif it feels like it has already asked too many questions 1320 and risksannoying the user. The system may also ask at least a minimum number ofquestions 1320 to avoid the user 1314 feeling that the system couldn'tpossibly have asked enough to make an intelligent decision.

The system may have a mechanism to tolerate incorrect answers from theuser. Incorrect answers may result from the user 1314 not understandingthe question, not understanding the answer 1322 or not knowing theanswer 1322 to the question. If the bulk of the answers given by theuser 1314 support a particular decision, the system may make thatdecision 1310 even though not all the user's answers support thatdecision.

In embodiments, the present invention may provide for at least one ofquestions 1320 and answers 1322 between the system and the user,decisions to users, and machine learning utilized to improve decisions.The system may provide for an improved way to generate questions 1320and answers 1322, an improved way to provide decisions to users, animproved way to utilize machine learning to improve questions 1320 anddecisions provided by a system, and the like, where any of thesecapabilities may be separately, or in combination, used as a standalonesystem or incorporated into a third party system as an improvedcapability. In embodiments, each of these improved capabilities mayutilize some form of machine learning as described herein. For example,the system may provide for an improved way to execute a question 1320and answer 1322 session with a user 1314 by learning under whatcircumstances the user 1314 is looking for certain information. Forinstance, it may be learned by the system that the weather is acondition under which users have a differentiated preference dependingon the time of day and the weather conditions. When it's raining duringthe day, and a user 1314 searches for movies, the user 1314 may be morelikely to be looking for movie tickets and locations where the movie isplaying. When it's raining during the night, and the user 1314 searchesfor movies, the user 1314 may be more likely to be looking for adescription of the movie. In another example, the system may provide foran improved way to provide decisions to users, such as learning thatusers prefer certain formats during the daytime versus during theevening, providing choices verses a single decision 1310 based on age,prefer a greater number of questions 1320 prior to presentation of thedecision 1310 based on the user's geographic location, and the like. Inanother example, the system may provide for an improved way to learnwhat decision 1310 to choose for a user, such as utilizing greaterexpert information based on age and education, utilizing popular opinionmore when the topic is fashion and the user 1314 is young versusutilizing traditional practice more when the user 1314 is older, askingmore questions 1320 about the user's choices in friends when the topicis personal, and the like.

In embodiments, the present invention may provide for combinations ofquestion 1320 and answer, providing decisions, and learning whatdecisions to provide, where one of the elements may not be provided bythe system, such as when that element is provided by a third partysystem. For example, a third party search engine web application maywhat to improve their capabilities for providing sorted lists from auser's search query, and so may want to utilize the present invention'sfacility for generating questions 1320 and answers 1322 to augment theirkeyword search and sort algorithms. In this instance, the third partysearch engine provider may not be interested in the present invention'sfacility for generating decisions, because their service is in thebusiness of providing sorted lists, not a limited set of decisions.However, the present invention may provide an important new capabilityto the search engine provider, in that the present invention's abilityto constantly improve the questions 1320 and answers 1322 to users mayenable the search engine provider to improve their sorting result tousers based on the present invention's capabilities.

In embodiments, the subject of the initial area of advice may bespecified through a search interface. For example, a user 1314 searchingfor “romantic honeymoons in Italy” may get taken to a web page thathelps the user 1314 decide where to honeymoon in Italy instead of firstasking the user questions about where they want to vacation, what typeof vacation they were looking for etc. Or a user 1314 could search for aspecific location in Italy and be directed to a web page on that 1)helps the user 1314 decide if that specific location is a good one fortheir needs (for example, showing things like “this vacation is good forhoneymooners and romantic getaways and bad for family vacations”) and 2)offers to start a dialog to help the user 1314 find alternative andpotentially better locations in Italy to vacation. Or a user 1314 couldbe searching for specific products and then enter into a dialog tonarrow down which of those products are best for them. In both cases #1and #2 the information shown may be based how other users have answeredquestions in decision making dialogs and then given positive feedback tothis decision. So if many people using the “where should I go onvacation” topic answered a question “do you want a romantic vacation”with “yes” and then gave positive feedback to “Italy” the system woulddisplay that Italy is a romantic destination to users 1314 coming in viasearch engines. Alternatively, the users 1314 who added the decision“Italy” or the question “do you want a romantic vacation” into thesystem could have explicitly indicated that the answer “yes” to thequestion “do you want a romantic vacation” should be associated withItaly and thus show that Italy is a romantic vacation to users 1314coming in via search engines.

In embodiments, the present invention may provide other combinations ofsome subset of asking questions, making decisions, and learning to makebetter decisions, such as using the present invention's facilities formaking better decisions, but only using input from experts; notproviding a question 1320 and answer 1322 session for a particular user,but rather utilize previous user 1314 interactions with the system toprovide decisions; asking questions 1320 and answers 1322 to a user 1314to allow the system to learn in association with future decisions, butproviding rewards to the user 1314 rather than decisions; askingquestion 1320 and answers and making a decision 1310 without anylearning, such as simply filtering down results; utilizing the presentinvention's ability to learn how to make a better decision, butproviding that capability to an expert system rather than to usersthrough a question 1320 and answer 1322 interface; and the like. Inembodiments, the system may provide for all the elements of a question1320 and answer 1322 user 1314 session, providing decisions to the user,and learning how to improve decisions.

In embodiments, a user 1314 entering a question 1320 may optionallyspecify dependencies and importances for the question. Dependencies maycontrol when the question can be asked. Importances may specify relativeimportances between different questions 1320 for weighing a user's 1314answers. If the system has to make trade-offs because no one decision1310 result matches all of the answers 1322 specified by a user 1314,the system may try to recommend decision results that match highimportance questions over lower importance questions. The system mayalso prioritize asking high importance questions over low importancequestions. For example, a user 1314 entering a new question like “Wherein the United States do you want to vacation” set a dependency thatrequires an existing question such as “Where in the world do you want togo” to have been answered with “The United States” before the newquestion “Where in the United States do you want to vacation” can beasked.

In embodiments, the present invention may provide for a system with auser 1314 interface through which the user 1314 may interact with thefacilities of the system. The system may include several parts, some ofwhich may be the website, the supervisor, and a collection of widgets.Widgets may be collections of code that collect, process, and render asingle piece of content on the website. The website may consist ofinterfaces for end-users, staff members, and registered users to getdecisions, edit the decisions, and view reports on system performance.The supervisor may be a container for running widgets so that a widgetcan perform time-consuming data collection and processing ahead of user1314 requests to render that content.

For example, a widget might collect videos about decisions from theinternet. The widget, in the supervisor, might crawl the web looking forvideos about each decision 1310 and store videos it finds in a database.When the user 1314 comes to the website and gets a particular decision,the website may ask the video widget to render itself and display anyvideos it has previously found.

A plurality of instances of the supervisor may be running on multiplecomputers in order to scale up the widget's processing. Each widget maybe running on its own computer. Similarly, many computers may beproviding interfaces to the system through web-servers, instantmessaging, voice gateways, email, programmatic APIs, via being embeddedin third party websites, and the like.

In embodiments, attributes may be combinations of a question 1320 andone particular answer 1322 to that question. For example, if a question1320 was “How old are you?” and the answers to that question 1320 were“under 18”, “20-30” and “over 30”, then an attribute would be “How oldare you? Under 18”. The system may work by learning the relationshipbetween attributes and decisions. When the system asks a question 1320and the user 1314 gives an answer 1322 then the system may take thatattribute and see which decisions are associated with it.

In embodiments, the system may understand that some attributes representcontinuous values while others represent discrete values. When usingcontinuous attributes, the system may be able to make more intelligenttradeoffs such as understanding that it is frequently acceptable torecommend a product that costs less than the user 1314 asked for butrarely acceptable to offer a product that costs more than the user 1314asked for.

In embodiments, the relationships between attributes and decisions maybe learned from users, explicitly given to the system or somecombination of the two, and the like. For example, a price attribute of“How much do you want to spend? Under $200” might be explicitly linkedto cameras that fall into that price range based on data from experts,ecommerce sites/APIs, etc. The relationship between the attribute “Howwill you use the camera? On vacations” and possible vacationdestinations might be fully learned however.

When entering new questions 1320, answers 1322, and results the user1314 may optionally specify the relationships between attributes anddecision results. For example, if a user 1314 were to enter the question“how much do you want to spend?” in the “which camera should I buy”topic, the user 1314 may also specify to the system that the answer“under $200” should be associated with cameras X and Y but not camera Z.Then, if a future user were to use the “which camera should I buy” topicand were to answer the “how much do you want to spend” question with theanswer “under $200” that user 1314 may have a higher chance of beingrecommended camera X and Y over camera Z.

After seeking advice from the system and receiving a decision result, auser 1314 may also be given reasons from the system as to why thatparticular decision result was recommended. This explanation may alsoallow the user 1314 to change the attributes for the decision result ifthe user 1314 believes that the decision result was recommended in errorby the system.

In general, the relationships learned may involve training from users,experts, employees, automated data feeds from third parties, or somecombination.

In embodiments, there may be various ways that the system can recommenda solution and select the next question 1320 to ask the user. Possiblemachine learning systems may be geometric systems like nearest neighborsand support vector machines, probabilistic systems, evolutionary systemslike genetic algorithms, decision trees, neural networks, associatedwith decision trees, Bayesian inference, random forests, boosting,logistic regression, faceted navigation, query refinement, queryexpansion, singular value decomposition and the like. These systems maybe based around learning from complete game plays (e.g., all attributesgiven by a user 1314 before getting a decision), the answers toindividual questions/subsets of game plays, only positive feedbacks,only negative feedbacks or some combination of the two. Additionally,the system may take into account previous interactions the user 1314 hadsuch as remembering previously answered questions, decisions that theuser 1314 liked or did not like, which areas of advice the user 1314previously sought advice in, etc. Additionally, the system may take intoaccount factors that are implicitly provided by the user 1314 such astime of day and date the user 1314 used the system, the user's IPaddress, client type (e.g., Firefox, IE, cell phone, SMS, and the like),and other such data.

In embodiments, the present invention may provide for a machine learningsystem that goes well beyond the capabilities of collaborativefiltering, such as through explicitly asking questions 1320 instead ofimplicitly learning based on a user's behavior, which may be much morepowerful since the system is not left trying to infer the user's intent,mood, etc. Also, choosing the questions 1320 to ask the user 1314 basedon what they've already answered may allow the present invention to zeroin on nuances that would otherwise be missed. The present invention mayhave the ability to explain decisions, such as providing decisionsbeyond simple extrapolations form past behavior such as in, ‘otherpeople who bought X, Y and Z also liked product A’. Instead, the presentinvention may be able to say the user 1314 should ‘do A because the user1314 said they wanted X, liked Y and believed Z’. In addition, thepresent invention may allow users to contribute new questions 1320 thatmay be useful, and then automatically learn under which contexts, ifany, the question 1320 is helpful. In another area of difference, thepresent invention's machine learning technology may be able to providedecisions in a great variety of user 1314 interest areas, wherecollaborative filtering has difficulties being applied tonon-product/media applications. For instance, collaborative filteringwould not be easily applied to helping a user 1314 make a decision 1310on a highly personal topic, such as whether they should get a tattoo, ora rare question 1320 such as whether a particular expense can bededucted on the user's tax return. The present invention may be capableof such applications. In embodiments, the present invention may be ableto use pre-programmed expert advice inter-mixed with advice learned froma group of users to make decisions to users.

In embodiments, the system may have a wiki web interface for editing allof the data on the system. The web interface may be used toedit/create/delete questions, answers, attributes, and solutions. Eachsolution may also have a variety of information associated with it,which may be shown on the decision page when that solution isrecommended. For example, when recommending a vacation in Cancun therecommendation page might show videos about Cancun. All of thisancillary data about the solution may also be editable through the wiki.

In embodiments, the wiki may be used to edit data collected by widgetsrunning in the supervisor. This may allow the widgets to collect dataahead of time and then have a human quality assurance process to reviewand change the collected data.

In embodiments, the system may maintain a history of all changes made byeither the widgets or humans. For example, one use of this history maybe to review the work done by hired contractors doing content qualityassurance. Another use of this history may be making sure that thewidgets do not undo work done by humans. For example, if the widgetscollect a particular video and a human deletes that video because it isinappropriate, then the widget can use the history to not re-add thatvideo again sometime in the future. Finally, if data is corrupted orincorrectly deleted the history may allow a means of recovery.

In embodiments, when widgets find new content they may queue tasks to ahuman workflow for validating and editing that content.

In embodiments, in order to learn, the system may sometimes make randomor semi-random decisions in hopes of recommending something that thesystem wouldn't have expected to be useful, but which may turn out to beuseful. If the system wants to use what it has already learned, then itmay not make random choices in which questions 1320 it asks and whichdecision 1310 it makes. There may be a tradeoff between using what isalready known, also referred to as exploitation, and potentiallylearning something new, also referred to as exploration. Exploitationmay lead to a more satisfied user, while exploration may make the systemsmarter.

In embodiments, one way to make this trade-off when selecting questions1320 to ask the user 1314 may be to ask questions 1320 that the systemis confident are useful in making a decision 1310 and then picking a fewrandom questions 1320 to ask. Another way to make the trade-off may beto have a fixed budget in every user 1314 interaction where a fixed setof questions 1320 are based on exploitation and the next set are basedon exploration.

In embodiments, decisions may also be explored or exploited. If thesystem wants to learn, it may show a random decision. Instead of showinga purely random decision, the system may also show a decision 1310 thatmeets some requirements specified by the user 1314 and is purelyexploring within the remaining requirements. For example, instead ofpicking a random camera to show the user 1314 the system could pick arandom camera that meets the user's price requirements. This may resultin more efficient training since the system may be less likely to show adecision 1310 that has no chance of meeting the user's needs. Ratherthan showing a random decision 1310 when exploring, the system may alsoshow both the exploited decision 1310 and an explored solution and getfeedback on each separately from the user. Alternatively, the systemcould inject a limited amount of randomness and pick a decision “like”what the system's best guess is. For example, the system may predictthat the user 1314 will like one particular camera but could insteadrecommend another similar but not identical camera in order to balancemaking a reasonable decision 1310 and still learning new informationfrom the user. In embodiments, the system may identify to the user 1314when it is asking questions 1320 or making decisions through explorationvs. exploitation, or it may not.

In embodiments, the system may be viewed as surveying users about thevarious things it is recommending. For example, the system may ask theuser 10 questions 1320 about the Canon SD1000 camera. This may provide arich set of data about each camera allowing the system to start buildinglists of what kind of user 1314 is likely to like this camera. Thesystem may build a ranked list of decisions for each attribute, such asfrom most likely to be liked to least likely to be liked, given thatattribute. For example, the system may build a list of cameras in orderlikely to be liked by people who say “How old are you? Over 50”. Thismay be shown by the system as the top 10 cameras for users over 50.Numerous of these top 10 lists may be constructed based on the system'sdata. These lists may also be combined to form new lists. For example,given the ranked lists of cameras for the attribute “How old are you?Over 50” and another list for the attribute “Why are you buying acamera? Travel”, the system may construct a new ranked list of camerasfor the “Over 50 year old users who want a travel camera”. Thesecombinations of top lists may be pre-generated, generated on-demand byincrementally asking the user 1314 to select new top lists, and thelike.

In embodiments, these “top lists” may be used for a variety of purposes.Some users may not want to answer a series of questions 1320 beforereceiving a decision. Instead, they may be able to browse through theselists and find a relevant decision. The system may have a large numberof top lists, such as thousands or tens of thousands, each of which mayhave their own web page. In addition, these pages may contain a largeamount of content that may be indexed by search engines and bring usersto the system's website. Alternatively, users 1314 may use a searchinterface in the system itself to find the area of advice they want adecision in. Various top lists may be used to short-cut the dialog byimplicitly answering some of the questions 1320 in the dialog based onthe toplist. For example, there could be an area of advice called“vacations” and a top list called “romantic honeymoon vacations inItaly” that servers as a short cut or gateway into the “vacations” topicwith several questions 1320 from the “vacations” dialog alreadyanswered: “Where do you want to go? Europe”, “Where in Europe do youwant to go? Italy”, “Are you traveling on a special occasion? Yes”,“What is the special occasion? honeymoon”. This may serve as analternate interface for the user 1314 to seek advice through atraditional search interface without engaging in a question and answerdialog.

In embodiments, various pages on the site may have self-containeddisplays of information called widgets. For example, the decision pagesmay have a widget that shows how other people who liked this question1320 answered various questions, videos/pictures about the decision,links to other web sites that have information about the decision,personalized pros and cons of this decision 1310 based on how the user1314 answered questions, lists of other decisions that a similar, listsof other decisions that would have been made had questions 1320 beenanswered differently, lists of awards/honors for this decision (such asConsume Reports recommended), and the like.

In embodiments, the system may allow users to navigate through theuniverse of decisions (e.g., cameras, vacation destinations, etc) alongdimensions that are not commonly available. For example, instead ofbeing shown a camera and only letting the user 1314 say “show memore/less expensive cameras” the system may let the user 1314 say “showme cameras that are more liked by young people”, “show me a camera thatis better for travel and less stylish”, and the like. Dimensions like“style”, “good for travel”, “bad for young people”, and the like, may begenerated as a side-effect by asking users questions 1320 and thenlearning what is a good decision 1310 given those answers.

In embodiments, navigating along alternative dimensions may be used as astarting point for the user 1314, instead of the user 1314 selecting anarea to seek advice in and then engaging in a dialog. The user 1314 maystart interacting with the system by using a search interface or anexternal search engine to search for a specific decision result, such asa product name or travel destination. The system would then show theuser information about that specific decision result and allow the user1314 to navigate to other decision results, engage in a dialog to refinewhat the user 1314 is looking for, or show the user 1314 informationthat the system has learned (through machine learning, expert advice orsome combination) about this specific decision result. For example, auser 1314 may use a search interface to navigate to a web page showinginformation on a Canon SD1100 camera. The system may show other camerasthat people looking for a Canon SD 1100 also like, allow the user 1314to find similar cameras along non-traditional feature dimensions such asa camera that is better for taking pictures of sporting events, as wellas show what the system knows about the Canon SD1100 such as “great fortravel”, “not good for people learning photography”, “Available forunder $200”, “Preferred by people who are consider themselves designconscious”, and the like.

In embodiments, another possible interface may be to show users a listof decisions and display a simple explanation for why each decision 1310is being made. For example, when recommending cameras the system mayshow three cameras and say that one is “cheaper”, one has “longer zoom”and the other is “better for travel”. This may help the user 1314 seealternatives that they may not have otherwise seen based on how theyanswered the questions 1320 leading up to the decision 1310.

In embodiments, users may be asked different types of questions, such asquestions 1320 about the item being recommended (price, color, etc),questions 1320 about themselves, and the like. The system maydifferentiate users along dimensions, such as psychographic dimensions,demographic dimensions, and the like. Properties of users that may bepredictive may include the user's age, sex, marital status, whether theylive in rural/urban areas, frequencies of church attendance, politicalaffiliation, aesthetic preferences, sense of irony/sense of humor,socio-economic background, taste, preference for neat or disorganizedlifestyle, degree to which they plan ahead of time, and the like.

In embodiments, it may be difficult to directly ask questions 1320 andinstead the system may try to measure things that are correlatedinstead. For example, instead of asking about income, the system mightask where the user 1314 prefers to shop (e.g., Wal-Mart, Target, Saks,etc). Aesthetics may be determined via showing pictures of art, livingrooms, clothes, and the like, and asking which style the user 1314prefers. In embodiments, pictures may take the place of the question(and the answers may be about how you react to the picture) or thepicture can take the place of answers to questions 1320 such as “Whichof the following best resembles the clothes you like to wear”.

In embodiments, the system may group questions 1320 by whether they areabout the item being recommended or about the user. The system mayexplain what type of questions 1320 it is asking in order to help theuser 1314 understand the value of otherwise surprising and potentiallyoffensive questions 1320 being asked. The system may also display othertypes of messages to the user 1314 while asking questions, such astelling the user 1314 how many questions 1320 remain, taunting the user1314 by saying the system can already guess what decision 1310 to make,and the like.

In embodiments, instant messenger (IM) systems may provide a naturalinterface to the question 1320 and answer 1322 dialog of the system. Forexample, a user 1314 may invite our system to their “buddy list” andthen initiate a dialog to get a decision 1310 over IM. The system may IMthe first question 1320 to the user, the user 1314 may then IM theiranswer 1322 back, and the like, until eventually the system IM'ed theuser 1314 a link to the decision, or directly IM'ed the name of thedecision 1310 to the user. In embodiments, other forms of communicationsmay also be used, such as cell phones, SMS, email, and the like.

In embodiments, the system, such as in the form of an application, maybe embedded in third party web sites. For example, the system could beput on a website that sells cameras and offer to recommend relevantcameras to the user. Alternatively, after the user 1314 searched forcameras and had a list of potential cameras they were interested in, thesystem could ask questions 1320 to help the user 1314 decide amongst thelist of cameras. For example, if all of the cameras that the user 1314was considering were good for travel the system would not ask about howthe user 1314 wanted to use the camera, but the system might realizethat asking whether interchangeable lenses were desired could be used torecommend one camera over another.

In embodiments, the system may make decisions in a plurality of topicareas, such as: products (e.g., cameras, TVs, GPS/navigation, homeaudio, laptops, bath & beauty, baby, garden/outdoor, automobiles,jewelry, watches, apparel, shoes, and the like), travel (e.g., where togo, where to stay, what region to visit, what to do there, and thelike), financial (e.g., which mortgage, whether to refinance, whichcredit card, whether something is deductible on taxes, what type of IRAto save in, asset allocation for investments, and the like), gifts forvarious holidays and occasions, other date-based decisions (what todress up for Halloween, and the like), personality (e.g., about a user'spersonality, about their relationships, their career, and the like),recommending the right pet, drinks and other aspects of night-life,books, movies, film,/music, concerts, TV shows, video games, where toeat, what to order, celebrity related such as which celebrity the user1314 is most similar to, recommending a gift, what neighborhood to livein, what to watch on television, and the like.

In embodiments, the system may be used to diagnose problems, such as inthe areas of technology/IT (e.g., computer, software, printers, homenetworking, wireless, business networks, performance issues, and thelike), medical/health, automotive, relationship or interpersonalproblems, home and building problems, and the like.

In embodiments, users of the system may be either anonymous or logged inusers. A logged in user 1314 may be one that has created an account onthe site. Logged in users may also have profile pages about them.Content on the profile page may include basic information about thatuser (nickname, picture, etc), decisions they have received and liked,decisions the system predicts the user 1314 will like even though theuser 1314 has not answered questions 1320 in that topic area, lists offacts about the user 1314 that the user 1314 has given so that they donot need to be repeated each time the user 1314 uses the system for adecision (e.g., the user's age or their aesthetic preferences can begiven once and remembered across different times the user uses thesystem), lists of tasks that the system thinks the user 1314 may bequalified and interested in doing via the wiki (such as reviewing newuser 1314 submitted content, fixing spelling errors in user 1314submitted content, reviewing new content found by the widgets, etc),other users with similar answers to questions, and the like.

In embodiments, users may also have various titles, ranks or levelswhich may affect what they can do on the system. For example, some usersmay be given the title of “moderator” in a particular topic which wouldallow those users to edit certain aspects of those topics. The ranks andtitles may be assigned manually or by through automatic means includingbeing based on how many decisions they have given, how many newquestions 1320 or solutions they have contributed to the system, howmany tasks they have accomplished using the wiki, how well they answer1322 certain questions 1320 in the various topics, and the like.

In embodiments, non logged in users may not have the benefit of usingthe system with a large selection of aesthetic or taste-basedpreferences already entered into their profiles. Based on learning ormanual training from logged in users 1314, the system may select someaesthetic questions to ask in question dialogs when non-logged in usersseek advice in particular topic areas. For example, based on logged inusers answering taste questions about themselves and then givingfeedback about which cars they like and don't like, the system may learnthat a question 1320 about whether the user enjoys gourmet dining isuseful to ask non-logged in users trying to decide between a Toyota anda Lexus. Using the attribute associations learned or manually specifiedby logged in users, the system may then adjust whether it recommends theToyota or the Lexus to the non-logged in user.

In embodiments, the system may learn from users submitting feedback ondecisions. Some users may either intentionally or unintentionally giveincorrect feedback. For example, a vendor may try to game the system tomake their product be highly recommended. Alternatively, a user 1314 whodoes not know much about video games may recommend a video game that inreality is not a good video game. The system may try to filter outfeedback from these users by a variety of means. The system may throttlethe number of feedbacks that a given user 1314 can submit (and have ahigher throttle limit if the user 1314 is logged in or has a highrank/title). The system may also throttle or weight feedback based onhow well the user 1314 answers certain ‘test’ questions 1320 during thequestion 1320 & answer 1322 phase in order to test the user's knowledgeof the subject and weigh feedback from knowledgeable users more thanunknowledgeable users. The system may also require the user 1314 to passa ‘captcha’ (Completely Automated Public Turing test to tell Computersand Humans Apart) before their feedback is counted or they get adecision. The system may also look at the series of answers given by theuser 1314 and weight the user's feedback based on that series ofanswers. For example, if the user 1314 either always clicked the firstanswer 1322 or the user 1314 clicked in a very improbable way, then thesystem may weight that user's feedback lower. Finally, the system maychange the weight of the user's feedback or decide to not show adecision 1310 based on the history of previous game plays. For example,the 10th time a user 1314 tries to get a camera decision 1310 the systemmay weight their feedback less than on the 9th time.

In embodiments, the system may include search engine optimization (SEO),the process of improving the system's website rankings within majorsearch engines. This process may be broken down into severalmostly-automated steps, such as discovering the keywords that users aresearching for, understanding the competition in the search engines tohave the site's page come up when users search for these words,understanding how search engines rank sites, understanding what changesto the system's website need to be made in order to increase the site'sranking for common searches, and the like.

In embodiments, discovering keywords that users may be searching for maybe found through different means, such as using keyword suggestion toolssuch as what Google and Yahoo provide, using data about historicalsearches licensed from third party data providers and crawling otherwebsites to see what words they use, and the like. Once these keywordsare found, the system may use the data in many ways, such as bidding onthose words via search engine marketing (SEM), developing content on thesystem's site about those keywords in hopes of getting search traffic inthe future, looking at how our competitors are using those samekeywords, and the like.

In embodiments, the system may understand what other sites are doing andhow they rank in the search engines by running keywords through thesearch engines and looking at who is advertising on each keyword andwhat the top natural search results are for each keyword. The sitesdiscovered through this process may be crawled to discover morepotential keywords. The system may also decide to develop new content oravoid a market based on this competitive information. If there are fewhighly ranked sites in a content area, the system may develop content inthat area.

In embodiments, the system may understand that paid advertisements thatbring users 1314 to the site are relatively cheap in one topic area ofadvice on the site and expensive in another. The system may thereforetry to advertise for the low-cost traffic, help those users 1314 withtheir decision, and then recommend that those users 1314 use the systemin a topic area that is expensive to advertise and buy traffic in. Forexample, the system may run ads for people who want to figure out whatdog breed they should buy, help those users 1314 decide what dog breedis right for them, and then direct them to figure out where they shouldbuy their pet medicines. The latter topic area being one that may beexpensive for the system to source traffic in due to expensive ad rates,while the former topic area may be relatively cheap, as few existingbusinesses may be competing for customers who want advice on what typeof dog to get.

In embodiments, the system may understand how search engines rank theirnatural (non-sponsored) search results by studying the relationshipbetween sites that come up when a search is done and factors of thosesites. Possible factors that may be correlated between sites that comeup with high ranking may be factors such as the content of the site,number and quality of other sites linking to the site, the type ofcontent on those other linking sites, and the like. From the prior step,the system may generate a list of site factors, ranked by their abilityto increase a sites ranking in the search engines, and the like. Thesystem may then use this ranked list to make changes to the site toincrease the probability that the site as a whole, or certain pages onthe site, will be highly ranked in the search engines.

Search engines may typically utilize a keyword index to find documentsrelevant to a user's query. In embodiments, the present invention mayutilize a “decision index”, which may also map user-input to relevantdocuments. The index may be built automatically, experts may hand buildthe index, the index may be learned through feedback from differenttypes of users who implicitly or explicitly decide to train the system,and the like. The results of the search utilizing the decision index,may be displayed as a list of documents, a single document, and thelike.

Referring to FIG. 1, an embodiment for a list of topics 102 in thesystem from which users may get decisions is presented, includingcameras, cell phones, coffee and espresso, drinks, favorite celebrity,GPS devices, grills, Halloween, laptops, personality, toe rings, TVs,vacations, video games, watches, and the like. In addition, there may bean indicator as to the number of decisions learned 104 from userratings, such as learned from 43,921 user ratings.

Referring to FIG. 2, an embodiment of an example question 1320 that thesystem may ask a user 1314 is provided. In this example the user 1314 isasking for a decision 1310 related to the purchase of a camera, and thequestion 1320 is “How much are you willing to spend?” The user 1314 maynow choose from the selection 204, such as to select between less than$200, up to $300, up to $500, more than $500, I don't know, and thelike. In addition, there may be an indication as to how many questions1320 may be asked 202, such as in “In 10 questions or less, get cameradecisions preferred by people like you.” In embodiments, the user mayalso offer their own question, their own answer, their own decision, andthe like, where the system may utilize this information in the currentor future decision session. In embodiments, the user 1314 may choose toskip the question 208, where the user 1314 may now be provided analternate decision based on a reduced amount of information availablefrom the user, the system may ask the user alternate questions 1320 tomake up for the skipped question 208, the question 1320 may have been atest question and will not affect the resulting decision 1310, and thelike.

Referring to FIG. 3, an embodiment of an example picture question 1320that the system may ask a user 1314. In this example, the system may beasking a question 1320 whose answer 1322 may better enable the system todetermine a personal characteristic of the user 1314. For instance, thequestion 1320 as illustrated asks “Which of these causes you the mostconcern?”, where the picture choices 304 are indicative of certaintopics, such as pollution, finances, national defense, health, and thelike. This question 1320 may be targeted to the current user or beinserted as an experimental question. In embodiments, the user 1314 maybe informed that the question 1320 is an experimental question 302, suchas shown in FIG. 3 with the header that reads, “Finally, please answerthe experimental questions submitted by another user.”

Referring to FIG. 4, an embodiment of an example of the type ofinformation 402 the system may show the user 1314 when making aparticular decision 1310 is presented. For example, the decision 1310may be for a certain camera, where information is provided about thecamera, such as a description, who uses it, the best cost for thecamera, how it compares 404 to other cameras, and the like. Inembodiments, other decisions 1310 may be provided, such as with arelative ranking 408, by a score, by a percentage matching, and thelike. The user 1314 may also be queried for feedback 1312, such as beingasked if the decision 1310 is a good decision. In addition, the user1314 may be provided with the opportunity to find out more about thedecision 1310, such as more about the product 410, best price finder412, websites to more advice, and the like.

Referring to FIG. 5 and FIG. 6, the user 1314 may be provided withvarious top lists 502 associated with a topic as described herein, suchas presented in association with a decision, in association with auser's request to view top lists, and the like.

In embodiments, the present invention may provide users with a home page700 including user 1314 identification 702, personal representation,past decisions made, future topics for consideration, decision 1310 tomake today 714, and the like. FIG. 7 provides an example of a user homepage 700, such as what the user 1314 sees when they are logged into thesystem account. Here, there may be a display of recent decisions thesystem recommended, lists of popular topics 708 to get decisions in, asearch interface 710 to find topics, status updates about the user 1314getting benefits for contributing to the system, recent activity 704,access to the user's profile 712, and the like.

FIGS. 8 and 8A provide an example of a user's profile 712 page showinginformation about them and their account. The user 1314 may manage userinformation 802, such as a user's email address, password, and the like.They may also answer questions 1320 about themselves and have theseanswers remembered 810 and automatically used when they use decisionmaking topics in the system. The user 1314 may also receive rewards 804,such as “badges”, and see them displayed as received in response tohelping other users, contributing to the system, and the like. Some ofthese rewards may be based on the quality of the user's contributions,on the quantity of contributions, and the like. In addition, users maybe assigned a demographic group 808 of people who answered questions1320 about themselves similarly.

In embodiments, users may be able to decide they want to contributeexpertise 902 to the system, such as in a ‘teach the system’ mode. FIG.9 shows an example of various links/pages that may allow a user 1314 tocontribute, such as giving the system training about various decisions,rating the quality of pictures and user-contributed prose, findingduplicate items and questions, contributing new decision making topics,contributing new questions 1320 to existing topics, and the like.

In embodiments, the user, after choosing a topic for the system to makedecisions for, may be asked questions. FIG. 10 provides an example ofhow a question 1320 may be presented 1000 to the user. As shown, thepresentation of the question 1320 to the user 1314 may provide differentelements, such as a topic heading 1002, a picture or illustrationassociated with the topic 1004, a question, a set of answer choices, andthe like.

After answering questions, the user 1314 may be provided an answer 1322or decision 1310 associated with the user's original question. FIGS. 11and 11A show an example of how a decision 1310 may be presented 1100 tothe user, and may include a primary decision, information summarizingthe decision, alternate decisions, variations on the decision, and thelike. In addition, the user 1314 may be provided with an opportunity toprovide feedback 1312 to the system, such as whether the user 1314agrees with the decision 1310 or not. The user 1314 may also be providedother suggested topics 1102, such as based on the current topic, answersprovide, history of answers, a user's profile, a user's history ofquestions, topics that other users found helpful, and the like.

FIG. 12 shows an example list of decisions 1200 in a topic. For aproduct topic, such as shown, the “decisions” may be what product tobuy. For other topics, the decision 1310 might be “yes, dump him” or“no, don't get a tattoo”. The decisions may be ranked and ordered basedon their relevancy to the user, based on how the user 1314 answeredquestions, based on how the user 1314 answered questions 1320 in thetopic, and the like. Additionally, the items may be ranked by price, byname, and the like.

FIG. 16 shows an example of a contributor/expert interface home page1600, showing recent contributions to the system 1602 and other usersmaking contributions 1604. In upper right corner is a question forlearning the user's taste preferences 1608.

FIG. 17 shows an example of a question in a dialog with the systemasking an objective question 1700, and in this instance, to a userlooking for help deciding what to name their new puppy.

FIG. 18 shows an example of a decision result showing the particularrecommended decision 1800 (in this instance, name your dog Rusty),reviews about this decision from other users 1802 (where it may beranked, such as by their similarity to the user), yes/no buttons 1804(such as for receiving feedback on this decision, showing other decisionareas that the user might enjoy under, and the like), suggested Topics1808. In this example, the system's second and third best recommendeddecisions are listed under the #2 tab 1810 and #3 tab 1812. The systemmay also be engaging in exploration by also recommending a “wild card”decision which may be a decision that was partly picked throughrandomness. The Suggested Topics 1808 may be selected based on howrelevant the system thinks these topics may be for the user and/or howmuch profit the system thinks it may be able to generate from the userusing these other decision areas.

FIG. 19 shows an example of an interface for users 1900 to set theassociations between attributes and decision results. In this example,the decision result “Rusty” should be associated with the attribute “Isthis name for a female, or male dog? Male”.

FIG. 20 shows an example of how a user may edit content in the system2000. In this example the user is able to edit a decision result: it'sname, description, URL for getting more information, etc.

FIG. 21 shows an example of how content that is editable by users mayalso have an interface for seeing prior revisions 2100 to the contentand showing the changes between two prior revisions. Users may alsorevert the changes made by other users if those changes are deemed to beirrelevant or unhelpful. In this case the example shows the differencebetween two revisions to a decision result where the description of theresult has been changed.

FIG. 22 shows an example showing a question being edited by a user 2200.New answers may be added, existing answers re-ordered, the question andanswer text itself edited, and the like. Questions may be optionally“locked” to prevent other users from changing them, such as by indicatedby the pad lock icon 2202.

FIG. 23 shows an example showing that edits to attributes may haverevision histories like other editable content 2300. This example showsthe difference between two revisions of the attribute associationsbetween the decision result “Rusty” and the attributes “How manysyllables do you want the name to have? No more than 2 or 3 or more isOK”.

FIG. 24 shows an example showing a ‘workshop’ screen 2400 where newlyadded areas of advice may be first displayed. In embodiments, expertusers may make additions here without regular users seeing theworks-in-progress. Content that is deemed objectionable, irrelevant, orlow quality may be voted on and removed from the system.

FIG. 25 shows an example showing the system asking the usertaste/subjective questions 2500 in order to learn taste and subjectivepreferences from the user. After answering these questions the systemmay show statistics on how other users answered the same question.

FIG. 26 shows an example of an activity feed 2600 of recent activity bycontributors across the site showing newly added content and experttraining.

In embodiments, the present invention may provide a facility forproviding an improved way to provide decisions to a user 1314 with aquestion 1320 across a broad category of topics, including products,personal, health, business, political, educational, entertainment, theenvironment, and the like. For example, the system may provide decisionson everything from whether a user 1314 should break up with theirboyfriend, to whether you should get a tattoo or not, to whether you candeduct something on your taxes in addition to product decisions, and thelike. In embodiments, the system may provide decisions on any interest auser 1314 may have.

In embodiments, the present invention may provide a decision system thatis flexible and is capable of changing and growing. This may be partlyenabled by the system's use of a dialog of questions 1320 and answers tomake a decision, and then getting feedback from the user 1314 so thesystem can improve. In embodiments, this approach may be significantlymore powerful since the system may ask any question 1320 and thereforeget much better information from the user 1314 about their wants. Inaddition, users may be able to extend the system by entering their ownquestions 1320 and answers for the system to ask, entering in newdecisions for the system to make, and the like. The system may thenautomatically try out newly entered information to see if it is usefulor helpful and use this new information to determine if it is useful,and possibly stop asking/using the questions/decisions that may not beas helpful to users. In embodiments, this approach may provide forbuilding a wisdom-of-the-crowds based decision making expert system forpotentially any topic.

In embodiments, the present invention may also provide improved decisionfacility to the user 1314 by providing decisions by ranking acrossnon-traditional feature dimensions. For example, instead of just rankingcameras by price or size, the system may rank cameras based on how muchthey're liked by retired people, how sexy they look, and the like. Thesystem may then help users navigate across these dimensions. Forinstance, instead of users just being able to say “I like this camera,but want a cheaper one” the system may let them do things like say “Ilike this camera but want one better for learning photography” or “Ilike this vacation, but want one with a more active social scene”.

In embodiments, the present invention may lend itself to a variety ofdifferent user interfaces, such as a web interface, instant messaging,voice, cell phone, SMS/instant messaging, third party use (e.g. a widgeton a third party, web service sold to a third party), and the like. Forexample, a voice interface may be well suited to the system since theremay be a very limited vocabulary that the system must recognize, such asjust the possible answers to each question. In this way, if the systemcan't understand a user response it may just move on to another question1320 instead of annoying the user 1314 by asking them to repeat theiranswer over and over. In another example, the present invention may beintegrated into a third party site, such as a search for a TV on ane-commerce website, where the present invention is a widget to help theuser 1314 narrow down the results, or using the present invention as awidget in association with a real estate website to build an MLS queryfor the user 1314 to find a house that is a good match for them. Inembodiments, the present invention may provide a user interface, both inregard to a physical interface and in the way questions, answers, anddecisions are presented, that provides the user 1314 with asignificantly improved way to obtain decisions on a great variety oftopics.

In embodiments, the present invention may be integrated into third partyproducts in such as way as to improve the third party's user interfaceand user satisfaction. For example some website services providepredictions through past purchase history. In this case, the presentinvention may be able to explore a user's mood or intent, such asthrough asking explicit questions. In the case of search engines, thepresent invention may be able to detect when the user 1314 is trying tomake a decision 1310 and then start to ask them follow on questions. Inthe case of forum sites, mailing lists, news groups, and the like, thepresent invention may provide improved access to decisions and decisionsthat were made by people similar to the user. For example, the presentinvention may be able to search through all the forum posts to findpeople who are in the same situation as the user, and providing whatdecision 1310 the forum community recommended to them.

In embodiments, the present invention may be able to extend e-commerceweb application user interfaces. For instance, a user 1314 may start aproduct search with a keyword search and then ask questions 1320 tonarrow down the results to the best decision 1310 for the user. Thepresent invention may be able to provide a Q&A interface for picking aproduct once the user 1314 clicks into a category page. For example,after clicking cameras on the website, the user 1314 might see a firstquestion. The present invention may be able to rank products alongdimensions that are based on how users answer questions. For example,cameras might be ranked from best to worst ‘travel camera’ based on howpeople answer 1322 the question “What do you want a camera for?” Answer“Travel” and then whether they give positive or negative feedback to aparticular camera. This may allow the e-commerce website to rank a listof camera keyword search results from best to worst travel cameras.

In embodiments, the present invention may be able to provide an improvedsearch engine capability, such as detecting when a user 1314 is tryingto make a decision 1310 and switching to a Q&A interface, based on thesearch results from a keyword search ask follow up questions 1320 tonarrow down or re-rank the results, ask questions 1320 in order to builda keyword search query or to refine a search query, learn feedback basedon which links a user 1314 clicks after being asked questions, and thelike. In addition, the present invention may implicitly learn about theuser 1314 and alter rankings based on these implicit facts, such as whattime of day they're using the system, where they are in the world, whattype of browser they're using, weather where they are, and the like.

In embodiments, the present invention may be able to provide a way forinformation to be gathered and utilized by users. For instance,Wikipedia is a way for users to contribute information such that the enduser 1314 must, to some extent, self validate the accuracy of theinformation subsequently supplied to them. In a similar fashion, thepresent invention may be able to host a web application that utilizesuser contributed content. For instance, instead of learning what theprices of cameras are, the web application could have users input theprices of cameras and then allow other users to self validate theseclaims. In this way, the scope of the contributed information may beallowed to grow organically as users interact with the system.

In embodiments, some e-commerce applications may provide for productsand/or services that are associated with personal preference, and so maybenefit from the present invention. For instance, there are currentlyseveral movie rental web services, where the user 1314 selects moviesfor delivery to their home through the mail. Decisions are also providedto the user 1314 based on what the user 1314 has selected in the past.However, choosing a movie may involve personal interests at the time ofrental that cannot be determined by past selections, such as mood,intent, weather, are they going to be alone or with someone, theircurrent personal relationships, and the like. These types of interestsmay be explored with the present invention through questioning, and assuch, may provide a much more personalized match to the user's interestsat the time of rental.

In embodiments, local search applications may be improved through theuse of the present invention. For example, if a user 1314 wanted adecision 1310 on where to eat dinner, they might search for “dinner innew york” and find a website with suggestions targeted to the query.This interface however falls short when the user 1314 doesn't have aclear idea as to what keywords to include. For instance, the user 1314might not know the key options for food and might not think to searchfor ‘ethiopian food new york.’ The present invention may have theadvantage of being able to figure out what question 1320 it should askin order to narrow down the possibilities. In embodiments, the presentinvention may be able to aid in the building of a search query.

In embodiments, the present invention may provide for an improved way tomatch up users and experts, users and other knowledge based users, andthe like. For instance, a service may be provided to collect users andexperts on different topics. Users may then come to the web interface ofthe service and enter into a session of Q&A where the best match isdetermined. As a result of the questions, the system may provide adecision, where the profile of the expert or other user 1314 isprovided, and where the user 1314 may be asked if they agree with therecommended individual. In embodiments, the user 1314 may be provided ahome page where previous matches and communications may be kept,forwarded to friends, experts rated, and the like.

In embodiments, the present invention may provide a platform for acommunity based question 1320 and answer 1322 application. For instance,users may post questions 1320 to the system, and other users may beallowed to respond. In such a system, a user 1314 may receive answersfrom a single user, multiple users, an automated system, and the like,where the user 1314 may be able to choose which answer 1322 they feel iscorrect. This answer 1322 may be kept private, posted for others toview, posted as the correct answer, provided to the system, and thelike. In embodiments, the system may use the questions 1320 and answersto further develop the system, provide more accurate answers to users,sort the answers provided to the user, filter the answers provided tothe user, and the like. In addition, users of the system may providefeedback to answers provided by other users, contribute to filteringcriteria for eliminating incorrect answers, and the like.

In embodiments, the present invention may be used as entertainment,through machine learning capabilities as described herein. For instance,a user 1314 may provide an input or think of an idea, such as a topic, akeyword, a category, a question, a feeling, and the like, and the systemmay make a guess as to what it is through a series of questions 1320 andanswers. For example, the user 1314 may think of an object, such asbaseball, and the system may utilize machine learning capabilities, suchas geometric systems, to provide questions 1320 to the user. A typicalquestion 1320 may relate to size, such as ‘is it bigger than a toaster?’These questions 1320 may then be answered by the user, such as throughmultiple choice selection, fill in the blank, true/false, free response,and the like. The system may then continue the question 1320 and answer1322 sequence until it has a guess, and provide this guess to the user.In embodiments, this process may continue for a fixed number ofquestions, a random number of questions, a user 1314 specified number ofquestions, a system determined number of questions, a system specifiednumber of questions, and the like. In embodiments, the system mayprovide the user 1314 with a user interface, such as through theInternet via a website, through a stand-alone computational device,through a mobile computational device, through a phone service, througha voice interface, in association with an instant messaging service,through text messaging, and the like. In embodiments, the system may beprovided to a third party, such as a widget to another website, as anAPI to a third party application, and the like. In embodiments, thepresent invention may use non-neural networks for entertainmentapplications, such as playing games.

In embodiments, the present invention may provide a system to assist inthe discovery of new drugs, where the system may provide an aid in theselection and combination of molecules in creating a new drug. Forexample, the system may ask the user 1314 about information associatedwith chemical parameters, such as solubility, reactivity, toxicity, andthe like, and combine these with questions 1320 to probe the user'sexpertise in recognizing molecular structures. As the question 1320 andanswer 1322 sequence progresses, the system may provide the user 1314with insights as to which molecular structures may be stable andsynthesizable. In embodiments, the process may continue until the user1314 has an improved sense for what molecular combinations may make fora new drug, until the choice of new exploratory routes are available forpresentation to the user, until an new potential drug is identified, andthe like.

In embodiments, the present invention may provide for an image finderapplication, where the user 1314 may be assisted in identifying an imagethat fits some subjective criteria that is not necessarily explicitlyknown to the user. For example, a user 1314 may be involved in thedevelopment of a brochure for a company, where they have the text forthe brochure, but need to select an image to support the ideas andemotions that the text is trying to convey. The user 1314 may in thisinstance have a subjective idea as to what type of photograph may berequired, but not necessarily to the extent that they could specify asearch with keywords. The user 1314 may instead first specify the sourceof the images, such as from a file, a database, a website service, fromGoogle images, from an advertiser image bank, and the like. Then theuser 1314 may be asked a series of questions, or be presented with aseries of images to choose from. The answers and/or selections that theuser 1314 chooses may then be utilized in refining the choices that arenext presented to the user, and from which further questions 1320 and/orimage selections may be provided. In embodiments, this process maycontinue until the user 1314 finds an image to select as the finalimage. Additionally, the system may take the user's ‘final selection’and select a group of other similar images for presentation to the user,at which time the user 1314 may choose to continue the process ofselection refinement.

In embodiments, the present invention may be used in a baby namingapplication, where the user 1314 may have only a vague sense of whatnames they might prefer. The user 1314 may be initially asked differenttypes of questions 1320 intended to provide the system with informationto aid in the learning of the user's preferences, such as questions 1320about family, friends, education, heritage, geographic location, placeof birth, hobbies, books read, movies watched, and the like. The systemmay then continue to learn through the presentation of questions 1320associated with name preference in a plurality of ways, such as ratingname, choosing from a list of names, answering questions 1320 pertainingto name, and the like. In embodiments, this process may continue untilthe user 1314 finds a name to select as the final name. Additionally,the system may take the user's ‘final selection’ and select a group ofother similar names for presentation to the user, at which time the user1314 may choose to continue the process of selection refinement.

In embodiments, the present invention may provide decisions for aplurality of topics including, but not limited to, video games, laptops,vacations, cameras, general personality, drinks, cell phones,televisions, grills, watches, coffee machines, toe rings, Halloween, GPSdevices, hottest celebrity, your personal hero, presidential election,baby toys, blogs, camcorders, cars, which star wars character are you,credit cards, hair care, skin care, sex and the city, should I get atattoo, professions, how much allowance, city to live in, dog breeds,fragrance, New York, neighborhood chooser, software, desktop computers,DVD players and recorders, cigars, charities, Broadway shows, speakers,home theater systems, MP3 players, computer networking devices,headphones, memory cards, magazines, books, Oprah picks, books, The NewYork Times bestsellers, business casual clothing, franchises, cookware,toys, toys—educational, athletic apparel, espresso machines, should I goGreek, should I come out to my parents, should I ask for a raise, do Ihave a drinking problem, should I medicate my add/ADHD child, vacuumcleaners, clothes washers and dryers, is working at a startup right forme, humidifiers, are you a good friend, risk of developing diabetes,which foreign language should I learn, microwaves, car audio, what kindof customer are you, wine, should I join the military, which militarybranch should I join, what kind of art will I enjoy, baby and toddlercar seats, baby strollers, baby travel accessories, natural and organicbeauty products, makeup, home audio receivers and amplifiers, copiersand fax machines, printers, breakup with my boyfriend/girlfriend, whichGreek god are you, what game show would I enjoy, computer accessories,which superpower should you have, college, online degree programs,choose a major for college, identity theft prevention, should I hire apersonal trainer, should I buy or lease a car, should I have laser eyesurgery, what should I do about losing my hair, should I start my ownbusiness, should my child start kindergarten, how to entertain my familyvisiting NYC, OTC pain relievers, do I need a living will, miles or cashfor my next flight, best way to whiten my teeth, should I let mydaughter wear makeup, is hypnosis likely to cure my bad habit, EDoptions, sleep aids, OTC allergy pills, how much money to spend on awedding gift, should I buy the extended warranty, is it better to takethe SAT or ACT, personal audio accessories, coffee/espresso drink wouldI enjoy, video game consoles, jeans, downloadable PC games, snacks,vitamins and supplements, which superhero am I, sunglasses, kitchengadgets, pillows, beauty accessories, beauty bags and cases, sportinggoods, which musical instrument is right for me, should I hire adecorator, electronic readers, where do you belong in a shopping mall,power washers, small business, phone system, how much to tip, should Itry Botox, should I get liposuction, risk of skin cancer, should Irefinance my home, car services (NYC), microbrewery beer, gourmetchocolates, am I saving enough for retirement, entertainment centers/TVstands, cookbooks, electric shavers, keep sending nieces/nephews bdaygifts, luggage, computer projectors, energy/workout bars, razors,gourmet ice creams, online dating, newscasts, makeup, tools and brushes,beauty mirrors and compacts, business books, how soon to call after afirst date, places to retire, external hard drives, universal remotecontrols, walking shoes, should I sell my life insurance policy, howgreen are you, do I have an eating disorder, baby cribs, diets and dietbooks, cell phone plans, wedding and engagement rings, am I assertiveenough, does my child play video games too much, tax preparation(personal return), should I get a reverse mortgage, cancel plans withfriends for a date, children's TV shows, kitchen countertops, bathingsupplies, insect repellents, cancer specialist, hospitals, nationalchain restaurants, cereal, should I have kids now, should I hire ananny, movies, beef cuts, target calories per day, do I have OCD, homeair purifiers, auto air fresheners and purifiers, i-phone applications,gay/lesbian vacations, is it ok to ask my co-worker on a date, is mypre-teen ready to babysit, sports/energy drinks, TV shows, officefurniture, motorcycles, reward a child for a good report card, lawntrimmers and edgers, am I too stressed, religion, do you make a goodfirst impression, do you spend too much time online, should I get a newhairstyle, should I home-school my child, diaper bags, should I usecloth or disposable diapers, dog toys, is my partner cheating on me,classic books should my elderly parent stop driving, am I over my ex, isit lust or love, pedometers and heart rate monitors, chewing gum,weather devices, will gas additives help my car, Orlando theme parks,how big of a turkey should I buy, popular music—new releases, selftanner, tax and money management, software, baby bottles and Sippy cups,baby high chairs and booster seats, baby tethers, toasters and toasterovens, comforters sheets and bed linens, flatware sets, pet carriers andkennels, cheese, kitchen faucets, casual shoes, dress shoes, beautyelectronics, am I saving enough for retirement, mutual fund chooser,steak cuts, what is my D&D alignment, acne and pimple medication,bathroom faucets, home exterior lighting, landscape lighting, lawnmowers, aperitif, cognac, gin, rum, scotch, tequila vodkas, whiskeys,Las Vegas shows, sunscreen, running shoes, US MBA programs, patio andoutdoor furniture, kitchen knives, are you a true fan, auto insurance,personal legal services, should I hire a financial advisor, indoor plantselector, delivery services, can I deduct it, pool heaters, sofas, housenumbers, contact lenses, birthday gifts, has my career peaked,electronic books, doorknobs & lock sets, snow removal equipment, greenhome improvement, kids clothing & swimwear, motorcycle helmets, bicyclehelmets, juicers, golf clubs, refrigerators, wine coolers, ranges andovens, air conditioners, Christmas gifts, breakup phrases, cold soremedication, diabetes monitoring devices, smoking cessation, what do I doabout the hair on my back, hormones to counteract menopause, hikingbackpacks, school backpacks, get a website/domain, e-mail services, webhosting, carpets, power tools, tile, water heaters, outdoor paint,window treatments, fireplace screens, indoor lamps, small business legalservices, brunch recipes, ceiling fans, mattresses, Las Vegas hotels andcasinos, salsas, love quiz for valentines, how much to spend on clientgifts, anniversary gifts, outdoors outerwear, casual outerwear, campingtents, sleeping bags, tires, adventure vacations, music downloads, videodownloads, wedding dresses, wedding themes, Manhattan gyms, budget hotelchains, golf courses, ski vacations, US spas, ETF funds, designerhandbags, should I declare bankruptcy, 401k as down payment on home,should I see a psychiatrist, self defense, dishware, dishwashers,political parties, new year's resolutions, cruise lines, familyvacations, baby food, baby health care products, should I shave my head,t-shirts, online photo services, buy a class graduation ring, summerjob/internship, where to volunteer, home alarm systems, diagnose yourrelationship issues, is she/he hot for me, should I adopt, should myaging parents be driving, online bank accounts, BBQ sauces, frozenpizza, recipe finder, should I re-gift it, bodybuilding supplements,home workout equipment, how many hours of sleep do I need, should Iconsider plastic surgery, risk of arthritis, risk of heart disease, riskof osteoporosis, do I have a gambling problem, best dance to learn,bicycles, cat food, dog food, hobby recommender, martial arts, posterart, outdoor flower selector, which Muppet are you, activities for kids,how ethical are you, should I baptize my child, Miami hotels, USnational parks, motor oils, automotive video, blouses, coats, dresses,glasses frames, hosiery, interview clothes, jackets, negligee, pants,shirts, skirts, hats, phones—land lines, steakhouses, which birth methodis right for you, summer camp recommender, march madness bracketchooser, baby formula, New York bakeries, fractional jet ownership, howself confident am I, digital photo frames, do I need an accountant, doesmy child have ADD/ADHD, document shredders, baby monitors, green homeimprovement, conference phones, and the like.

In embodiments, and as depicted in FIG. 13, the present invention mayhelp a user 1314 make a decision 1310 through the use of a machinelearning facility 1302. The process may begin with an initial question1320 being received 1304 by the machine learning facility 1318 from theuser 1314. The user 1314 may then be provided with a dialog 1308consisting of questions 1320 from the machine learning facility 1318 andanswers 1322 provided by the user 1314. The machine learning facility1318 may then provide a decision 1310 to the user based on the dialog1308 and pertaining to the initial question 1304, such as arecommendation, a diagnosis, a conclusion, advice, and the like. Inembodiments, future questions 1320 and decisions 1310 provided by themachine learning facility 1318 may be improved through feedback 1312provided by the user 1314.

In embodiments, the initial question 1304 posed by the user 1314 may bean objective question, a subjective question, and the like. A question1320 may be provided from amongst a broad category of topics, such astopics pertaining to a product, personal information, personal health,economic health, business, politics, education, entertainment, theenvironment, and the like. The questions 1320 may be in the form of amultiple choice question, a yes-no question, a rating, a choice ofimages, a personal question, and the like. The questions 1320 may beabout the user 1314, provided by another user, provided by an expert,and the like. The questions 1320 may be based on a previous answer, suchas from the current dialog 1308 with the user 1314, from a storedprevious dialog 1308 with the user 1314, from a stored previous dialog1308 with another user. The question 1320 may be a pseudo randomquestion, such as a test question, an exploration question 1320 thathelps select a pseudo random decision 1310 on the chance that the pseudorandom decision 1310 turns out to be useful, and the like. The questions1320 may include at least one image as part of the question. Thequestions 1320 may be along psychographic dimensions. In embodiments,the questions 1320 may not be asked directly to the user 1314, butrather determined from contextual information, such as through an IPaddress, the location of the user, the weather at the user's location, adomain name, related to path information, related to a recent download,related to a recent network access, related to a recent file access, andthe like.

In embodiments, the dialog 1308 may continue until the machine learningfacility 1318 develops a high confidence in a reduced set of decisions,such as a reduced set of decisions presented to the user, a singledecision 1310 presented to the user. The decision 1310 provided by themachine learning facility 1318 may be independent of the order ofquestions in the dialog 1308. The decision 1310 may provide an alternatedecision 1310 when at least one question 1320 in the dialog is omitted,where the alternate decision 1310 may be different based on the machinelearning facility 1318 having less information from the user 1314. Thedecision 1310 may display a ranking of decision choices, such as rankingdecisions across non-traditional feature dimensions. The decision 1310may display at least one image related to the decision 1310. Thedecision 1310 may be a pseudo random decision on the chance that thepseudo random decision 1310 turns out to be useful, such as the pseudorandom decision being part of a system of exploration, where the systemof exploration may improve the effectiveness of the system, the machinelearning facility 1318 may learn from exploration, and the like.

In embodiments, the feedback 1312 provided may be related to, or derivedfrom, how the user 1314 answers questions 1320 in the dialog 1308, howthe user 1314 responds to the decision 1310 provided by the machinelearning facility 1318, and the like. In embodiments, the feedback 1312may be solicited from the user 1314.

In embodiments, users 1314 may extend the learning of the machinelearning facility 1318 by entering new information, where the newinformation may be their own topic, question, answer, decision, and thelike. The machine learning facility 1318 may use the new information todetermine whether the new information is helpful to users.

In embodiments, a user interface may be provided for user interactionwith the machine learning facility 1318, such as associated with a webinterface, instant messaging, a voice interface, a cell phone, with SMS,and the like.

In embodiments, the present invention may help a user make a decision1310 through the use of a machine learning facility 1318. The processmay begin with an initial question 1304 being received by the machinelearning facility 1318 from the user 1314, where the initial question1304 may be associated with one of a broad category of topics, such asproduct, personal, health, business, political, educational,entertainment, environment, and the like. The user 1314 may then beprovided with a dialog 1308 consisting of questions 1320 from themachine learning facility 1318 and answers 1322 provided by the user1314. The machine learning facility 1318 may then provide a decision1310 to the user 1314 based on the dialog 1308 and pertaining to theinitial question 1304, such as a recommendation, a diagnosis, aconclusion, advice, and the like. In embodiments, future questions 1320and decisions 1310 provided by the machine learning facility 1318 may beimproved through feedback 1312 provided by the user 1314.

In embodiments, and as depicted in FIG. 14, the present invention mayhelp a user make a decision 1310 through the use of a computing facility1402. The process may begin with an initial question 1304 being receivedby the computing facility 1418 from the user 1314. The user 1314 maythen be provided with a dialog 1408 consisting of questions 1320 fromthe computing facility 1418 and answers 1322 provided by the user 1314.The computing facility 1418 may then provide a decision 1310 to the user1314 based on an aggregated feedback 1428 from the feedback from aplurality of users 1412. In embodiments, the computer facility 1418 mayimprove future questions 1320 and decisions 1310 provided by thecomputing facility 1418 based on receiving feedback 1412 from the user.

In embodiments, the present invention may help a user make a decision1310 through the use of a machine learning facility 1318. The processmay begin with an initial question 1304 being received by the machinelearning facility 1318 from the user 1314. The user 1314 may then beprovided with a dialog 1308 consisting of questions 1320 from themachine learning facility 1318 and answers 1322 provided by the user1314, where the number of questions 1320 and answers 1322 providedthrough the dialog 1308 may determine the quality of the decision 1310.The machine learning facility 1318 may then provide a decision 1310 tothe user based on the dialog 1308 and pertaining to the initial question1304, such as a recommendation, a diagnosis, a conclusion, advice, andthe like. In embodiments, future questions 1320 and decisions 1310provided by the machine learning facility 1318 may be improved throughfeedback 1312 provided by the user. In embodiments, the quality may behigh when the number of questions 1320 and answers 1322 large, such asgreater than 10 questions, greater than 15 questions, greater than 10questions, and the like. In embodiments, the quality may be good qualitywhen the number of questions 1320 and answers 1322 is small, such asless than 10 questions, less than 5 questions, less than 3 questions,one question, and the like.

In embodiments, and as depicted in FIG. 15, the present invention maymake a decision 1310 through the use of a machine learning facility1318. The system may include a machine learning facility 1318 that mayreceive an initial question 1304 from the user 1314, a dialog facility1502 within the machine learning facility 1318 providing the user 1314with questions 1320 and accepting answers 1322 from the user, themachine learning facility 1318 providing a decision 1310 from a decisionfacility 1504 to the user 1314, and the like. In embodiments, thedecision 1310 provided to the user 1314 may be based on the exchange ofdialog 1308 between the user 1314 and the machine learning facility1318, and pertain to the initial question 1304. Further, the machinelearning facility 1318 may receive feedback 1312 through a feedbackfacility 1508 from the user 1314 to improve future questions 1320 anddecisions 1310 provided by the machine learning facility 1318.

In embodiments, the present invention may help a user 1314 make adecision 1310 through the use of a machine learning facility 1318. Theprocess may begin with an initial question 1304 being received by themachine learning facility 1318 from the user 1314 through a third party,such as a search application, a social network application, a serviceprovider, a comparison shopping engine, a media company's webenvironment, and the like. The user 1314 may then be provided with adialog 1308 consisting of questions 1320 from the machine learningfacility 1318 and answers 1322 provided by the user 1314. The machinelearning facility 1318 may then provide a decision 1310 to the user 1314based on the dialog 1308 and pertaining to the initial question 1304,such as a recommendation, a diagnosis, a conclusion, advice, and thelike. In embodiments, future questions 1320 and decisions 1310 providedby the machine learning facility 1318 may be improved through feedback1312 provided by the user 1314.

In embodiments, the present invention may help a user 1314 make adecision 1310 through the use of a machine learning facility 1318. Theprocess may begin with an initial question 1304 being received by themachine learning facility 1318 from the user 1314 through a third partysearch application, where the user 1314 begins with a keyword search onthe third party search application and then is provided a dialog 1308consisting of questions 1320 from the machine learning facility 1318 andanswers 1322 provided by the user 1314. The machine learning facility1318 may then provide a decision 1310 to the user 1314 based on thedialog 1308 and pertaining to the initial question 1304, where thedecision 1310 may be provided back to the third party searchapplication, such as in the form of a sorted list.

In embodiments, the present invention may help a user 1314 make adecision 1310 through the use of a machine learning facility 1318. Theprocess may begin with an initial question 1304 being received by themachine learning facility 1318 from the user 1314. The user 1314 maythen be provided with a dialog 1308 consisting of questions 1320 fromthe machine learning facility 1318 and answers 1322 provided by the user1314, where the machine learning facility 1318 may utilize third partyinformation, functions, utilities, and the like. The machine learningfacility 1318 may then provide a decision 1310 to the user 1314 based onthe dialog 1308 and pertaining to the initial question 1304, such as arecommendation, a diagnosis, a conclusion, advice, and the like. Inembodiments, third party information, functions, utilities, and thelike, may include an application programming interface (API) enablingthe collection of cost information, product information, personalinformation, topical information, and the like.

In embodiments, the present invention may help a user 1314 make adecision 1310 through the use of a machine learning facility 1318. Theprocess may begin with an initial question 1304 being received by themachine learning facility 1318 from the user 1314 through a third partysearch application, where the user 1314 begins with a keyword search onthe third party search application and then is provided a dialog 1308consisting of questions 1320 from the machine learning facility 1318 andanswers 1322 provided by the user 1314 The machine learning facility1318 may then provide a decision 1310 to the user 1314 based on thedialog 1308 and pertaining to the initial question 1304, such as arecommendation, a diagnosis, a conclusion, advice, and the like. Inembodiments, the decision 1310 may be provided back to the third partysearch application based at least in part on collaborative filtering.

In embodiments, the present invention may help a user 1314 make adecision 1310 through the use of a machine learning facility 1318. Theprocess may begin with an initial question 1304 being received by themachine learning facility 1318 from the user 1314. The user 1314 maythen be provided with a dialog 1308 consisting of questions 1320 fromthe machine learning facility 1318 and answers 1322 provided by the user1314. The machine learning facility 1318 may then provide at least oneimage with the decision 1310 to the user 1314 based on the dialog 1308and pertaining to the initial question 1304, such as a recommendation, adiagnosis, a conclusion, advice, and the like. In embodiments, the imagemay be a photograph, a drawing, a video image, an advertisement, and thelike.

In embodiments, the present invention may help a user 1314 make adecision 1310 through the use of a machine learning facility 1318. Theprocess may begin with an initial question 1304 being received by themachine learning facility 1318 from the user 1314. The user 1314 maythen be provided with a dialog 1308 consisting of questions 1320 fromthe machine learning facility 1318 and answers 1322 provided by the user1314 where the questions 1320 may be determined at least in part fromlearning from other users of the machine learning facility 1318. Themachine learning facility 1318 may then provide a decision 1310 to theuser 1314 based on the dialog 1308 and pertaining to the initialquestion 1304, such as a recommendation, a diagnosis, a conclusion,advice, and the like. In embodiments, the decision 1310 may be based atleast in part on learning from decisions 1310 provided by other users ofthe machine learning facility 1318.

In embodiments, the present invention may help a user 1314 make adecision 1310 through the use of a machine learning facility 1318. Theprocess may begin with an initial question 1304 being received by themachine learning facility 1318 from the user 1314. The user 1314 maythen be provided with a dialog 1308 consisting of questions 1320 fromthe machine learning facility 1318 and answers 1322 provided by the user1314. The machine learning facility 1318 may then provide a decision1310 to the user 1314 based on the dialog 1308 and pertaining to theinitial question 1304, such as a recommendation, a diagnosis, aconclusion, advice, and the like. In embodiments, the decision 1310 maybe based at least in part on collaborative filtering.

In embodiments, the present invention may help a user 1314 make adecision 1310 through the use of a machine learning facility 1318. Theprocess may begin with an initial question 1304 being received by themachine learning facility 1318 from the user 1314. The user 1314 maythen be provided with a dialog 1308 consisting of questions 1320 fromthe machine learning facility 1318 and answers 1322 provided by the user1314. The machine learning facility 1318 may then provide a decision1310 to the user 1314 based on the dialog 1308 and pertaining to theinitial question 1304, such as a recommendation, a diagnosis, aconclusion, advice, and the like. In embodiments, the decision 1310 maybe based at least in part on collaborative filtering whose context isprovided through the dialog 1308, such as at least one questionproviding the context for the collaborative filtering.

In embodiments, the present invention may help a user 1314 make adecision 1310 through the use of a machine learning facility 1318. Theprocess may begin with an initial question 1304 being received by themachine learning facility 1318 from the user 1314. The user 1314 maythen be provided with a dialog 1308 consisting of questions 1320 fromthe machine learning facility 1318 and answers 1322 provided by the user1314. The machine learning facility 1318 may then provide a decision1310 to the user 1314 based on the dialog 1308 and pertaining to theinitial question 1304, such as a recommendation, a diagnosis, aconclusion, advice, and the like. In embodiments, the decision 1310 maybe based only on information gathered through a plurality of user 1314 sof the machine learning facility 1318 and pertaining to the initialquestion 1304, where at least one of the plurality of user 1314 s of themachine learning facility 1318 may be the user 1314 associated with thedialog 1308.

In embodiments, the present invention may help a user 1314 make adecision 1310 through the use of a machine learning facility 1318. Theprocess may begin with an initial question 1304 being received by themachine learning facility 1318 from the user 1314. The user 1314 maythen be provided with a dialog 1308 consisting of questions 1320 fromthe machine learning facility 1318 and answers 1322 provided by the user1314. The machine learning facility 1318 may then provide a decision1310 to the user 1314 based on the dialog 1308 and pertaining to theinitial question 1304, and with limited initial machine learningfacility 1318 knowledge on the subject matter of the initial question1304. In embodiments, the limited initial machine learning facility 1318knowledge may be seed knowledge, may be limited to basic knowledgeassociated with the subject matter of the initial question 1304, may belimited to basic knowledge associated with the subject matter of theinitial question 1304 where the basic knowledge may be expert knowledge.

In embodiments, the present invention may help a user 1314 make adecision 1310 through the use of a machine learning facility 1318. Theprocess may begin with an initial question 1304 being received by themachine learning facility 1318 from the user 1314. The user 1314 maythen be provided with a dialog 1308 consisting of questions 1320 fromthe machine learning facility 1318 and answers 1322 provided by the user1314. The machine learning facility 1318 may then provide a decision1310 to the user 1314 based on the dialog 1308 and pertaining to theinitial question 1304, such as a recommendation, a diagnosis, aconclusion, advice, and the like, where the decision 1310 may be basedon learning from a combination of expert and user inputs.

In embodiments, the present invention may help a user 1314 make adecision 1310 through the use of a machine learning facility 1318. Theprocess may begin with an initial question 1304 being received by themachine learning facility 1318 from the user 1314. The user 1314 maythen be provided with a dialog 1308 consisting of questions 1320 fromthe machine learning facility 1318 and answers 1322 provided by the user1314. The machine learning facility 1318 may then provide acategory-based decision 1310 to the user 1314 based on the dialog 1308and pertaining to the initial question 1304, such as a recommendation, adiagnosis, a conclusion, advice, and the like.

In embodiments, the present invention may help a user 1314 make adecision 1310 through the use of a machine learning facility 1318. Theprocess may begin with an initial question 1304 being received by themachine learning facility 1318 from the user 1314. The user 1314 maythen be provided with a dialog 1308 consisting of questions 1320 fromthe machine learning facility 1318 and answers 1322 provided by the user1314. The machine learning facility 1318 may then provide a decision1310 to the user 1314 where the machine learning facility 1318 mayutilize responses from a plurality of user 1314 s of the machinelearning facility 1318 to categorize and provide decisions 1310 along atleast one of psychographic and demographic dimensions.

In embodiments, the present invention may provide a user 1314 with aresponse through the use of a machine learning facility 1318. The user1314 may be provided with a dialog 1308 consisting of questions 1320from the machine learning facility 1318 and answers 1322 provided by theuser 1314, where the questions 1320 from the machine learning facility1318 may be related to an application, such as an entertainmentapplication, a drug discovery application, a baby name application, andthe like. The machine learning facility 1318 may then provide theresponse to the user 1314 based on the dialog 1308 and pertaining to theinitial question 1304, such as a recommendation, a diagnosis, aconclusion, advice, and the like. In embodiments, future questions 1320and decisions 1310 provided by the machine learning facility 1318 may beimproved through feedback 1312 provided by the user 1314.

In embodiments, the present invention may provide results based onmultiple dimensions, such as a result based on a textual match from auser input, based on the user's taste profile, and the like. FIG. 27shows an example search interface showing search results for the userquery “fios”. In this example, the rankings are based on first findingdecisions and decision results (recommendations) that are a good textualmatch for the user query and then second ranking the decision results bythe knowledge the system has about the user's taste profile. In thisexample, “fios” is a good textual match for the decision results“Verizon FIOS” as the recommendation to “Which ISP should I use” 2702and “What US satellite/cable service provider should I get” 2704 andboth are this users #1 ranked result based on the user's tastepreferences.

In embodiments, the present invention may provide multiple question andanswer ‘results’ in response to a user's unstructured input. Forexample, FIG. 28 shows a case where the user's query is ‘suv’. The firstresult is for the topic ‘What new car should I buy?’ 2802 but with thequestion ‘what type of car do you want?’ already answered with ‘suv’.This may provide a bridge between unstructured search and the structuredQ&A data that the system stores. Further, as shown in the example, theuser's top 3 results are displayed personalized based on their tasteprofiles. Effectively, the user has done a keyword search and gottenresults without explicitly answering any questions via the traditionalQ&A interface, such as for the other questions provided as examplesshown, ‘Which BMW should I buy?’ 2804, ‘What late model used car shouldI buy?’ 2808, and ‘What tires should I use on my car or truck?’ 2810.

In embodiments, the present invention may ask the user to express theirpreferences when they have given answers to questions that eithercontradict each other, are mutually exclusive, or which eachindividually have a dramatic affect on the rankings of results for theuser. For example, the user may start the “What new car should I buy”topic and answered that they want a SUV that is under $18,000 and ismore practical than extravagant. The system may want to get the user'spreference as to what is more important—that the vehicle be an SUV orunder $18,000. FIG. 29 shows an example question to the user, asking fortheir preference.

In embodiments, the system may learn a set of question importances thatare per-user, per-decision-result,per-question's-answer-per-decision-result, and the like. For example,the system may learn that user A cares more about weight than price whenit comes to buying small cameras, but cares more about price thananything else when it comes to buying cars.

In embodiments, users may extend the system by adding new decisionresults. Those new decision results may optionally include links to webpages to read more about the decision result. The system mayautomatically convert those links to affiliate links such that thesystem receives commissions from the site that the link points to.Further, based on the link submitted by the user, the system mayrecognize what kind of link it is and understand how to parse outinformation such as price for products, such as from Amazon.com, orparse out product codes so that vendor-specific API calls can be made tolook up product information based on the product codes.

In embodiments, users may be clustered into groups using dimensionreduction techniques such as singular value decomposition (SVD),eigenvector, and other like based approaches. The system may displayinformation about why a group of users were clustered together. One wayto do this is to find the top X dimensions in the low-dimension spacethat the cluster as a whole differs most on from the population average.The divergence of the cluster's distribution of answers from the generalpopulation's distribution in each dimension in the sub-space may be usedto rank dimensions in terms of how well they explain what is uniqueabout each cluster.

In embodiments, the dimensions in the sub-space may not be easilydescribed or interpreted due to their being composed of many differentfeatures such as how people answered questions or which decision resultsthey like. One way to explain what each dimension means may be to findquestions and answers that are most correlated with the differentextremes of the dimension and label the dimension with thesequestions/answers.

In embodiments, one way to cluster users may be to pick an initialrandom grouping of users and iteratively move users between clusters tominimize how much users differ from each other in their own cluster.After some number of iterations the process may be stopped or theprocess may continue until a threshold amount of error has been reached.

In embodiments, the present invention may facilitate matching by usersimilarity. Given a username, email address, numeric user id, and thelike, provide a list of other users who are similar or dissimilar insome way. For example, given a Facebook username provide a ranked listof other Facebook users who have similar tastes over all or in somespecific way, such as in electronics. In addition, this list may beoptionally restricted, such as to other users being one degree away inthe social graph from the first user (e.g, rank my friends on Facebookby their similarity to me so that I can ask the one most like me aquestion). In embodiments, user similarity may be computed via asking auser questions about themselves, looking at their social graph, usingcontext like their location, IP address, time, and the like. The socialgraph may be used by mapping users in the social graph to known users inother data sets based on heuristics on username, email address, firstname, last name, birthday, address, sex, and other like information.Adjacent people may be searched in the social graph to bring more peopleinto consideration even if they are more removed from the person you aretrying to ‘triangulate’ in on. For example, combining a social graphfrom Facebook with users who have written reviews on Amazon to findusers on Facebook who are most like me and then look at what laptopsthey tend to like on Amazon in order to give me a laptop recommendation.In another example, reviews may be filtered on a site such as Yelp,Tripadvisor, Amazon, and the like, based on people similar to you. Thisinformation may then be used to help the user, such as recommendingusers to “friend” on Facebook, to ‘follow’ on Twitter, and the like.FIGS. 30 and 31 provide examples of similarity profiles as may beprovided by the present invention.

In embodiments, the present invention may provide a level of indirectionbetween recommendations by instead recommending people who in turnlike/dislike things. The present invention may recommend things to buybased on what similar users bought on Amazon, places to eat based onsimilar users on Yelp, Zagat, Foursquare, and the like, things to clickon in Google search results based on what similar people clicked on, andthe like. For example, consider the problem of recommending which usersa new user to Twitter should follow. The present invention may look atall the users on Twitter and who follows them, and match some of thosefollowers to the data set of the present invention knowing things abouteach user based on the questions they have answered about themselves.This could also match the followers to other users, such as Amazonusers, Yelp users, and the like, to learn other things about them. Basedon this, the present invention may make inferences about the followersof a particular user, such at a Twitter user. Now, a new user may beasked about themselves and find which existing user's followers this newuser is most like. A recommendation may then be generated for the newTwitter user to follow the existing Twitter user whose followers aremost like the new user.

In embodiments, the present invention may facilitate real-timepersonalization, such as making recommendations that immediately reflectnew information from a user, immediately use their social graph, newfacts about them, their context such as changing location etc. tore-rank recommendations or otherwise improve results.

In embodiments, the present invention may match based on context, suchas location, time, weather, social graph, and the like, such asimplicitly using location to show nearby places a user might like toeat, drink, sites they might like to see, things to do, etc. Forexample, this process may then be used in a mobile application that hasaccess to location data via GPS. Optionally, the recommendation may beinformed by other parts of the user's context, such as the currentweather (e.g. don't recommend a place that people only like because ofthe patio if it's raining), current time (e.g. don't recommendnightclubs at 10 am), calendar (e.g. know when user is busy and wherethey have to be in the future), social graph (e.g. recommend places thatthe user's friends are at all else being equal), and the like.

In embodiments, the present invention may provide for a natural languagequestion and answer interface, such as to allow freeform or structuredinput from a user about a decision or recommendation they want helpwith. The input may be categorized either by asking the user, havingother users review the question, using automated techniques like naturallanguage processing (i.e., “is this question about electronics, travel,cars or some other topic?”), and the like.

In embodiments, the present invention may find similar users to therequesting user who has expertise in the category of the question. Forexample, the user may want advice about hotels in L.A. from people whohave similar taste in hotels AND know something about L.A. hotels (suchas either self-described knowledge or demonstrated knowledge based ontheir actions). For example, the present invention may then alert thosesimilar users to the new question and requesting their help in solvingit. Consideration may be given to how many prior questions/alerts theyhave been sent, how many they have already responded to, how helpfultheir responses have been, and the like. Similar users may be allowed toengage in a dialog with the requesting user to help inform therequesting user's decision or recommendation problem. The resultingdialog may be stored for others to use and encourage the similar usersto index the dialog into a structured form to aid later recall by otherrequesting users.

The present invention may provide third-party sites with the ability tolearn about their users, finding similar users, and makingrecommendations, such as independently of direct user interaction withthe system. In embodiments, the present invention may provide a tasteand preference API that third parties, such as third parties hostingtheir own web sites, may use to learn about the tastes, preferences,likes, dislikes, and other attributes of a user, where the user is notengaged in a dialog or interacting directly with the computing facilityof the present invention. For example, a user may go to a website suchas Amazon.com and make an inquiry about a product. In this instance,Amazon may have an API of the present invention that enables thecreation or enhancement of a taste and preference profile of a user soas to better determine the user's tastes, preferences, and the like, andso enable the third-party to better target meaningful responses back tothe user with regard to the user inquiry. In embodiments, thethird-party may use the API to determine the tastes, preferences, andthe like of the user without user interaction, such as by determiningtheir tastes and preferences through previous interactions with theuser, where these previous interactions may be from previousinteractions while on the third-party site, such as Amazon in thisexample, or from previous interactions with other websites hosting asimilar API or through direct interaction with a facility of the presentinvention. In embodiments, the API may be used by a plurality of userssuch that interactions with the users may be used to identify othersimilar users, and so use the choices, decisions, selection,recommendations, and the like, of these other similar users to aid inthe selection of recommendations to the present user. In embodiments,these other similar users may be associated with the third-party hostingthe API, or from another host API site, or from a facility of thepresent invention. In embodiments, the use of an API of the presentinvention hosted by a third-party site may provide a significantadvantage to the third-party site with regard to recommendations thatare relevant to the tastes, preferences, likes, dislikes, attributes,and the like, of the user.

In embodiments, tastes and preferences of a user may be determined oraugmented though other users, such as other similar users, other usersthat are connected to the user in a social network, other users that areassociated through a personal or professional activity, other users thatare friends or family, and the like. In embodiments, this may be donewithout the need to ask questions of the user. For instance, a user mayhave an existing taste and preference profile as established through thepresent invention, and that profile may be improved by collecting orinferring information about other users in their social network, family,place of business, and the like. In embodiments, the user's profile maybe improved through inferring additional tastes and preferences fromother similar users, or users shown to have some connection to the user,such as through a social network. In embodiments, the user's profile maybe improved through choices made by other similar users, such as in aproduct selection, recommendation, and the like. In embodiments, thesystem may learn about a known user's taste profile though their ratingthings they like and don't like, or through the use of natural languageprocessing, such as inferring a taste profile by analyzing how the usertags their user profile.

Referring to FIG. 32, in embodiments the present invention may providefor a computer program product embodied in a computer readable mediumthat, when executing on one or more computers, helps a third-partywebsite to learn about a user through the use of a computer facility3202 by performing the steps of: (1) providing a user preferencelearning API to the third-party website 3220 to determine preferences ofthe user 3218 as applied to a market of the third-party, wherein thepreference learning API is executing as an extension of the computerfacility 3204; (2) receiving third-party information related to themarket of the third-party 3208; (3) collecting the preferences of theuser 3218 and storing them as a user preference profile 3210; (4)receiving a query from the user at the third-party website 3220associated with the market of the third-party 3212; and (5) supplying arecommendation to the third-party based on the user preference profileand the third-party information to aid the third-party in the answeringof the received query 3214. In embodiments, the determining preferencesmay be through the use of natural language processing. The computingfacility may be a machine learning facility. The third-party informationmay consist of at least one of product information from productmanufacturers, product information from web merchants, pricinginformation from other websites, availability information from otherwebsites, pricing information from merchants, availability informationfrom merchants, a review, comments, and ratings. The preference learningAPI may enable the collection of at least one of cost information,product information, personal information, and topical information.Further, user profile preferences may be additionally based oninformation inferred from a user's social network, where the user maynot receive additional dialog between the user and the computerfacility.

Referring to FIG. 33, the present invention may provide for the use of ataste and preference API to target responses returned to a user, such asfor targeting advertising, show reviews from similar users, recommendproducts or services, show similar people on social networks, to ranksearch results based on which results similar users clicked on most, andthe like. In embodiments, the present invention may provide for acomputer program product embodied in a computer readable medium that,when executing on one or more computers, helps target responses returnedto a user through the use of a computer facility 3302 by performing thesteps of: (1) providing a user preference learning API to a third-partywebsite 3320 to determine preferences of a user as related to a marketof a third-party, wherein the preference learning API is executing as anextension of the computer facility 3304; (2) receiving third-partyinformation related to the market of the third-party 3308; (3)collecting the preferences of the user 3318 and storing them as a userpreference profile 3310; (4) receiving a query from the user 3318 at thethird-party website 3312; and (5) using in the user preference learningAPI the third-party information related to the market of thethird-party, and the preferences of the user 3318 as stored in the userpreference profile to provide a response back to the user that relatesto the query from the user 3314. In embodiments, the computing facilitymay be a machine learning facility.

The response may be providing an advertisement to the user, where theadvertisement may be based on the preferences of the user as stored inthe user preference profile. The advertisement is provided by thecomputer facility, provided through the third-party and enabled throughpreferences provided to the third-party from the computer facility,delivered to other users in a user's social network, and the like. Theresponse may provide a recommendation of a product, service, and thelike related to the market of the third-party. Collecting thepreferences of at least a second user may form a user preference profilefor the second user, determining the second user to be similar to theuser based on a comparison of preference profiles. The response may beproviding a recommendation made by the second user. The collecting ofpreferences for the second user may be taken from an internet basedsocial construct, and the response provides information to the user thatshows the second user as a similar person on the construct, where theinternet based social construct may be a social network. The collectingof preferences for the at least second user may include search resultselections, the query may be a search request, and the response may be asearch result ranked according to the search result selections of the atleast second user. The collecting may be from recommendations,purchases, and search result choices made by the user. The collectingmay be from sources that reveal location behaviors of the user. Thesource may be a user location information, such as from the web servicesfoursquare, yelp, Google, Gowalla, Facebook, and the like. The sourcemay be user location information from a service provider. Thethird-party information may consist of at least one of productinformation from product manufacturers, product information from webmerchants, pricing information from other websites, availabilityinformation from other websites, pricing information from merchants,availability information from merchants, a review, comments, andratings. The determining preferences may be through the use of naturallanguage processing. The API may enable the collection of costinformation, product information, personal information, topicalinformation, and the like. The collecting may be from a user'sinteractions as represented in an Internet based social interactiveconstruct, where the internet based social interactive construct may bea social network. The response may be a list of reviews sorted by areview author's similarity to the user reading the reviews. Collectingthe preferences of the user may be through third party websites. Thecollecting may be through crawling third party websites.

In embodiments, the present invention may utilize a taste and preferenceAPI that third parties may use to target advertising to a user based onthe user's preferences, where the user is not engaged in a dialog orinteracting directly with the computing facility of the presentinvention. For instance, a user may have previously undergone a dialogof questions and answers with the system, and through that dialog thesystem may have developed a taste and preference profile for the user.In embodiments, the dialog may have been provided directly with afacility of the present invention, or through a third-party API providedby the present invention. Alternately, the user may have neverinteracted with a facility of the present invention, where the user'staste and preference profile may be created and updated through theuser's interactions, responses, recommendations, reviews, and the like.In embodiments, the system may learn about a known user's taste profilethough their rating things they like and don't like, or through the useof natural language processing, such as inferring a taste profile byanalyzing how the user tags their user profile. The taste and preferenceprofile for the user may then be used to target advertising to the user,such as advertising that is matched to the user's tastes andpreferences. For example, a third-party taste and preference API may beassociated with an outdoor store website, such as L.L. Bean, REI, EMS,and the like, where the outdoor store is trying to improve theirtargeting of advertising to their customers. A customer may then visitthe outdoor store website and make a query for a product, such as forhiking boots. The taste and preference API may then enable a look up ofthe user's tastes and preference in order to establish a match for anadvertisement placement to the user's browser. In this example, theuser's taste and preference profile may indicate that the user enjoystraveling to New England, likes to camp, has a family with children, andthe like. As a result, the advertisement facility associated with thewebsite may select an advertisement that utilizes the information in theuser query, in this instance hiking boots, and information from theirtaste and preference profile. An advertisement in this case may be forlodging in the White Mountains, which combines the user's hiking bootquery with their preference for travel in New England. Further, thelodging may be a family lodging because of the user likes to travel as afamily, and with attributes that mirror the user's preferences. Inembodiments, the third-party taste and preference API may enable athird-party to improve their targeting of advertisements to users suchthat they are able to increase revenue made on a given advertisementplacement from an advertisement sponsor. In embodiments, the user tasteand preference profile may be developed in real-time as focused on theirimmediate query. Tastes and preferences may be gleaned so as to bettertarget advertisements to the user, such as during subsequent refinementof product search, at the point of purchase, and the like.

In embodiments, advertising may be targeted to a user or a group ofindividuals associated with the user based on taste and preferencesinferred though the user's social network. For instance, a taste andpreference API used by a third-party may be used to establish taste andpreferences for a group, node cluster, and the like, such as in a user'ssocial network. In embodiments, the tastes and preferences inferred fromthe social network may utilize taste and preference profiles previouslyformed, such as though third part sites or through a facility directlyassociated with the present invention. These taste and preferences maythen be used to better target advertisements to the user or to membersof the user's social network. In an example, a third-party may want totarget advertisement to a user, where the user has an established tasteand preference profile stored in a facility of the present invention.The third-party may then use information in the user's profile to targetadvertisements. Alternately, the third-party may additionally useinformation inferred from a social network that the user is part of,such as information pertaining to the topics of the social network,common interests of users associated with the user in the socialnetwork, and the like. For instance, the user may have a taste andpreference profile that indicates they are middle aged, politicallyconservative, rural, and the like, and is associated with users in asocial network that have hunting as a dominate interest. In thisinstance, the third-party may target advertisements for huntingequipment, hunting trips, and the like, where the advertisement has beenselected based on the user's existing taste and preference profile plusinferences from the user's social network. In embodiments, this may bedone without the need to engage the user in a dialog as describedherein, but rather indirectly through the user's interactions, such ason the third-party website, through third-party supplied information, onother websites where the present invention has a third-party API, andthe like. In embodiments, the third-party may also utilize the taste andpreferences from the user to target advertisements to other members ofthe user's social network.

In embodiments, advertising to a user may be targeted based on theproduct choices, recommendations, and the like, of users with similartastes and preferences. For example, a first user may have similartastes and preferences to a second user, where the first user has anexisting taste and preference profile and has made certain productchoices, recommendations, and the like. Advertisements may then betargeted to the second user based on the decisions of the first user.For example, a first user may have a profile that indicates they areolder, retired, lives in California, enjoys travel, and the like, wherethey have previously made a product choice for luggage. A second usermay then be provided a recommendation for similar luggage based on thesimilarity of the tastes and preferences of two users. In embodiments,this may be executed without the need to engage either of the users in adialog.

In embodiments, the present invention may provide for a computer programproduct embodied in a computer readable medium that, when executing onone or more computers, helps target advertising to a user through theuse of a computer facility by performing the steps of: (1) providing auser preference learning API to a third-party website to determinepreferences of the user as applied to a market of a third-party, whereinthe preference learning API is executing as an extension of the computerfacility; (2) receiving third-party information related to the market ofthe third-party; (3) collecting the preferences of the user and storethem as a user preference profile; (4) receiving a query from the userat the third-party website associated with the market of thethird-party; and (5) providing an advertisement to the user, wherein theadvertisement is based on the ascertained preferences of the user. Inembodiments, the determining preferences may be through the use ofnatural language processing. The advertisement may be provided by thecomputer facility. The advertisement may be provided through thethird-party and enabled through preferences provided to the third-partyfrom the computer facility. The API may enable the collection of atleast one of cost information, product information, personalinformation, and topical information. The decision may be also based oninformation inferred from a user's social network. An advertisement maybe delivered to other users associated with the user, such as through asocial network.

In embodiments, the present invention may provide a taste and preferenceAPI that third parties may use to provide users with reviews fromsimilar users, where the user and similar users may not have engaged ina dialog or interacting directly with the computing facility of thepresent invention. For instance, the taste and preference API may enablethe present invention to collect taste and preference information forthe user, provide the third-party with taste and preference informationfor the user from a previously established taste and preference profile,provide the third-party with taste and preference information for theuser based on near-term actions of the user, and the like. Inembodiments, the user may have never interacted with a facility of thepresent invention, where a user's taste and preference profile may becreated and updated through the user's interactions, responses,recommendations, reviews, and the like. In embodiments, the system maylearn about a known user's taste profile though their rating things theylike and don't like, or through the use of natural language processing,such as inferring a taste profile by analyzing how the user tags theiruser profile. In this case, similar users may have a previouslyestablished taste and preference profile, and as such may have a tasteand preference profile that may be matched to the user. In addition,these similar users may have reviews associated with their profile. Thesystem may now match the user to a similar user, and then provide theuser with the associated review. For instance, a user may have anexisting taste and preference profile with the system, such as directlywith the computer facility or though at least one third-party API, andmay want to know what other similar users thought of some product,service, person, event, and the like. The system may then search thetaste and preference profiles for similar users on the subject thepresent user has interest in. In this way, the system may now be able toprovide reviews and such to the present user from similar users, andthus helping the present user determine what they may want to do basedon their tastes and preferences. For example, a user may go to a productwebsite that utilizes the taste and preference API of the presentinvention, and is interested in reviews for digital cameras. Thethird-party may now find similar users, and then search for digitalcamera reviews by those similar users and provide the reviews to thecurrent user. In embodiments, the reviews may be resident at athird-party facility, at another third-party facility, at a facility ofthe present invention, and the like. In embodiments, the ability to showreviews of similar users may allow the user to access more relevantreviews in a more time efficient manner, and the third-party user of theAPI may be able to provide more targeted and relevant support to theirusers.

In embodiments, the present invention may provide a taste and preferenceAPI that third parties may use to provide users with reviews fromsimilar users, where the users are determined to be similar without thesimilar users participating in a dialog through the present invention.For instance, the similar user may be identified as being similarthrough a social network, friend, family, work, and the like. In anexample, a user may be associated with a second user though a socialnetwork, and through this association, determined to be ‘similar’, suchas though age, interests, and the like. The similar user may thenprovide a review, such as for a product, an activity, and the like. Thisreview may then be provided to the user as relevant though thesimilarity to the other user. In embodiments, similar users may bedetermined through similar recommendations on other topics, such as incombination with other factors, where the other factors may be a socialassociation.

In embodiments, the present invention may provide for a computer programproduct embodied in a computer readable medium that, when executing onone or more computers, helps a user find reviews of similar usersthrough the use of a computer facility by performing the steps of: (1)providing a user preference learning API to a third-party website todetermine preferences of the user, wherein the preference learning APIis executing as an extension of the computer facility; (2) collectingpreferences of a plurality of users, wherein the plurality of usersincludes the user, (3) storing the preferences of the user in a tasteand preference database which contains a plurality of taste andpreference profiles; (4) receiving a request from the user through athird-party taste and preference learning API for a topical review froma user who has similar taste and preferences; (5) matching thepreferences of the user to at least one other user's preference in thetaste and preference database; (6) searching for a review related to therequest for the topical review from amongst the matched other users; and(7) providing the review to the user. In embodiments, the determiningpreferences may be through the use of natural language processing. Thereview may be found within the computer facility, a facility of thethird-party, and the like. The computing facility may be a machinelearning facility. The preference learning API may enable the collectionof cost information, product information, personal information, topicalinformation and the like. The review may be provided by a similar userthat has no taste and preference profile, where the user may be similaras determined though a social association, where the social associationmay be a social network.

In embodiments, the present invention may provide a taste and preferenceAPI that third parties may use to recommend products, services, and thelike. For instance, a user may come to a third-party website in searchof a recommendation for a product, and the third-party may then utilizethe taste and preference API to better understand what the usertypically prefers, and from that preference, suggest a product. In anexample, the user may come to an audio store website looking for arecommendation for an audio system for playing music from their iPhone.The third-party may then utilize existing taste and preferences for theuser through the API. In this example, the tastes and preferences of theuser may indicate that they are a collage student and often on the runin their social life. From this information, the third-party may nowmake recommendations, such as recommendations for audio systems that areportable, small, powerful, and the like. Alternately, the third-partymay use the taste and preference API to determine their taste andpreferences at the time of the user inquiry, such as targeted to theinquiry, content of the third-party, for the user in general, and thelike. The third-party may use this new taste and preference informationalone, or in combination with previous tastes and preference profilesthrough the present invention, to make the recommendations. The tasteand preferences as established through the third-party may now be storedin a facility of the present invention, such as to be used again or incombination with new taste and preference profiles generated throughother third-party APIs or directly through a facility of the presentinvention. In embodiments, the ability to use the taste and preferenceAPI may improve the recommendations for products, services, and the likethat are made through third-party sites.

In embodiments, the present invention may provide a taste and preferenceAPI that third parties may use to recommend products, services, and thelike, to a user based on the actions of similar users. For instance, twousers may have previously established taste and preference profiles withthe present invention, where one of the users has selected a product,service, or the like, and where third-party may now provide arecommendation to the other user based on their similarity, such asdetermined through their profiles. In an example, two users may havebeen determined to be similar through their taste and preferenceprofiles, such as by their age, location, political views, socialactivities, and the like. The first user may then select a product, suchas a car. In the event that the second similar user should indicate aninterest in a car, such as through searching, advertisement selection,explicit question to the present invention, and the like, the presentinvention may provide the car selection to the second user as apotential fit because of their similarities. In embodiments, this may bedone without a dialog provided to one or both users.

In embodiments, the present invention may provide for a computer programproduct embodied in a computer readable medium that, when executing onone or more computers, helps a user find a recommendation through theuse of a computer facility by performing the steps of: (1) providing auser preference learning API to a third-party website to determinepreferences of the user as applied to the products and services of thethird-party, wherein the preference learning API is executing as anextension of the computer facility; (2) receiving third-partyinformation related to the products and services of the third-party; (3)collecting the preferences of the user and storing them as a userpreference profile, wherein the source of the collecting is from userinteractions on the Internet; (4) receiving a query from the user at thethird-party website associated with at least one of products andservices of the third-party; and (5) providing a recommendation for atleast one of the product and service to the user from the computingfacility, wherein the recommendation is based on the query and theascertained preferences of the user. In embodiments, the determining ofpreferences may be through the use of natural language processing. Thecollecting may be from the third-party website on the Internet; aplurality of third-party websites on the Internet; at least one ofrecommendations, purchases, and search result choices made by the user;and the like. The computing facility may be a machine learning facility.The third-party information may consist of product information fromproduct manufacturers, product information from web merchants, pricinginformation from other websites, availability information from otherwebsites, pricing information from merchants, availability informationfrom merchants, a review, comments, ratings, and the like. The API mayenable the collection of cost information, product information, personalinformation, topical information, and the like. The preferences may bederived from an action of a second similar user, where the similaritymay be determined through a taste and preference profile for the userand second similar user. The action of a second similar user may be aselection of at least one of product and service.

In embodiments, the present invention may provide a taste and preferenceAPI that third-party social network sites may use to show a user peoplethat are similar to them on the social network. These similar people maybe shown as a list, as photos, by region, by age, by gender, and thelike. For instance, the user may come to a social network site and askto see or be connected to people who are similar to them. The socialnetwork site may then utilize the taste and preference API to providethe user with a dialog to determine their tastes and preferences, suchas in general, to social situations, to social networking, toactivities, to music, to personality, and the like. Alternately, theuser may already have a taste and preferences profile as determineddirectly by a facility of the present invention, through anotherthird-party API, though the social network site, and the like. Thesocial network may then use this information to match the user to otherpeople on the social network, such as through taste and preferenceprofiles of the other people as previously determined, throughinformation available about the other people as available through thesocial network, and the like. For example, the user's taste andpreference may indicate that they are young and enjoy going to clubs inthe NYC. The social networking site may now be able to match the user tosimilar people on the social network, such as by list, photograph, bycategory, by region of the city, and the like. In embodiments, the tasteand preference API with a social network may provide an enhancedmatching experience to the user who is trying to find other similarpeople to be social with.

In embodiments, the present invention may provide for a computer programproduct embodied in a computer readable medium that, when executing onone or more computers, helps a user find other similar users on a socialnetworking site through the use of a computer facility by performing thesteps of: (1) receiving an initial request from a user through athird-party social networking site API, wherein the initial request isto find other similar users to them on the social network; (2)ascertaining preferences of the user through the social networking siteAPI; (3) matching the user preferences to other users on the socialnetwork with users with similar preferences; and (4) providing amatching result to the user that includes the other users that matchuser's preference. In embodiments, the ascertaining of preferences maybe through the use of natural language processing. The matching resultmay be presented to the user as a list of the similar users. Thematching result may be presented to the user as a profile of the similarusers. The matching result may be presented to the user as links to thesimilar users within the social network. The computing facility may be amachine learning facility.

In embodiments, the present invention may provide a taste and preferenceAPI that third-party searching facilities may use to rank search resultsbased on which results similar users selected the most. For instance,the searching facility may offer users the opportunity to improve therelevancy of how the search results are listed through a tastes andpreference profile, as provided through the taste and preference API ofthe present invention. A taste and preference profile database or thelike may then be accumulated and maintained, from which the searchingfacility may rank search results for the user to previously selectedresults by other similar users. In an example, a user may have a tasteand preference profile that shows a retired male who likes to sail andis a bit adventurous. When the user searches for Caribbean vacationdestinations the searching facility may rank the search results withthese taste and preference attributes listed first, such as for sailboatrental packages in the islands, hiking in the islands, off-beatdestinations, and the like. In embodiments, the use of a taste andpreference API provided to a searching facility may improve therelevancy of ranked search results to the user.

In embodiments, the present invention may provide for a computer programproduct embodied in a computer readable medium that, when executing onone or more computers, helps rank search results through the use of acomputer facility by performing the steps of: (1) receiving a searchrequest from a user through a third-party searching facility; (2)ascertaining preferences of the user, wherein the ascertainedpreferences from the user creates a taste and preference profile for theuser and is stored in a taste and preference storage facility whichincludes a plurality of other user taste and preference profiles, wherethe profiles also contain a history of search results selected by theother users in previous searches; (3) matching the user to other userswith similar taste and preference profiles; (4) determining a searchresult set for the user's search request; (5) matching the search resultset to the history of search results selected by the other users withsimilar taste and preference profiles; and (6) providing the searchresults to the user, wherein the search results are ranked according tothe matched results selected by the other users with similar taste andpreference profiles. In embodiments, the ascertaining of preferences maybe through the use of natural language processing. The computingfacility may be a machine learning facility. The searching facility maybe a search engine.

Referring to FIG. 34, the present invention may utilize social graphs toinfer the taste and preferences for an unknown user by finding pathsthrough an Internet based social interactive construct to people withknown taste preferences. In this way, the present invention may providea way to get data for a user that the system has never heard of before.In embodiments, the present invention may provide for a computer programproduct embodied in a computer readable medium that, when executing onone or more computers, helps determine an unknown user's preferencesthrough the use of internet based social interactive graphicalrepresentations on a computer facility 3402 by performing the steps of:(1) ascertaining preferences of a plurality of users who are part of aninternet based social interactive construct, wherein the plurality ofusers become a plurality of known users 3404; (2) determining theinternet based social interactive graphical representation 3412 for theplurality of known users 3408; and (3) inferring the preferences of anunknown user present in the internet based social interactive graphicalrepresentation 3412 of the plurality of known users based on theinterrelationships between the unknown user and the plurality of knownusers within the graphical representation 3410. In embodiments, theInternet based social interactive graphical representation may be asocial network, a social graph, a social diagram, and the like. Theunknown user may be three degrees, five degrees, and the like away fromthe closest known user in the internet based social interactivegraphical representation. The inferred preferences of the unknown usermay make the unknown user a new known user, and the new known user maybe used to contribute to the inferring of the preferences of a secondunknown user. The preferences may include personal information, topicalinformation, and the like related to interactions of the user, where theinteractions may be through the internet based social interactivegraphical representation. The interactions may be through an APIprovided to a third-party website. The inferring may be provided inconjunction with other known users that are related to the user in theuser's internet based social interactive construct. The ascertaining ofpreferences may be through the use of natural language processing. Thecomputing facility may be a machine learning facility. The inferredpreferences may be used to target advertising to the unknown user, toshare reviews with the unknown user. The inferred preferences may beused to recommend products, services, and the like to the unknown user.The inferred preferences may be used to aid in ranking search resultsfor the unknown user. Known users that are in close proximity to theunknown user may carry more weight in an inferring algorithm. Theinferred preferences may be refined by information from other sources,where the other sources may include third party sources, recommendationsmade by the plurality of known users, search queries by the plurality ofknown users, search result selections one of the plurality of knownusers, personal tastes as determined through web interactions by atleast one of the plurality of known users, and the like. The othersources may include a third-party preference learning API.

In embodiments, the present invention may utilize social network graphs,diagrams, graphical representations, and the like, to infer the tasteand preferences for an unknown user by finding paths through a socialnetwork to people with known taste, or visa versa. Social diagrams are,generally speaking, the mapping of a plurality of users and how they arerelated. By using the social diagram, a taste and preference of a knownand unknown user may be determined from their interrelation within thediagram. For instance, a user with a known taste and preference profilemay be directly associated with a plurality other users, such asrepresented in a social diagram. To a first approximation, it may beassumed that these plurality of other users are similar to the user, andso have similar taste and preferences. These other users may then beprovided refined services that take advantage of knowing a users' tasteand preference, such as described herein. For example, provided with auser with a known taste and preference profile indicating they are arock climber, it may be assumed that users within a first link of theuser's social diagram are also rock climbers. In reality, this may proveto be too general an assumption. However, it may be a good assumptionthat the user does have associations with other rock climbers, and sothe system may go out through the social diagram searching for otherknown users that enjoy rock climbing. In this example, it may be foundthere is another known user, such as three links away, that also enjoysrock climbing, and this user is found in a cluster that connects to thefirst user. From this it may be a good assumption that this cluster is agroup of rock climbers, and rock climbers may all share a set of similartastes and preferences with each other. In embodiments, tastes andpreferences may be inferred from associations within a social networkdiagram, and as such, may be provided benefits from the presentinvention as described herein.

In embodiments, the present invention may provide for a computer programproduct embodied in a computer readable medium that, when executing onone or more computers, helps determine an unknown user's tastes andpreference through the use of social network graphical representationson a computer facility by performing the steps of: (1) ascertainingpreferences of the user, wherein the user becomes a known user; (2)determine the social network graphical representation for the knownuser; (3) determine the presence to other known users within the knownuser's social network graphical representation; and (4) infer thepreferences of an unknown user present in the known user's socialnetwork graphical representation based on the interrelationships betweenthe unknown user and the known user and other known users within thenetwork graphical representation. In embodiments, the ascertaining ofpreferences may be through the use of natural language processing. Thesocial network graphical representation may be a social graph, a socialdiagram, and the like. The computing facility may be a machine learningfacility.

In embodiments, the present invention may combine the tastes andpreferences of a user as determined through two or more third-party APIto improve recommendations provided through the two or more third-partyAPI. For instance, there may be taste and preference profiles beingestablished through more than a single third-party API, and by combiningthese different tastes and preference profiles by the present invention,a combined taste and preference profile may be generated. Further, asadditional taste and preference profiles are created through third-partyAPI, they may be used to continuously update the combined taste andpreference profile for a user. The third parties may then utilize thecombined taste and preference profile to improve their recommendations.This may especially be the case when different third parties focus tasteand preference profiling on different areas, such as products, personalrelationships, services, celebrities, and the like. It can beappreciated that combining a number of more specific profiles into acombined profile may provide a richer taste and preference profile thencould be generated through any one of the more specific profiles. Inaddition, a user may change their tastes and preferences over time, andso combining more recent user profile interactions on one third-partyAPI may benefit another third-party that the user has not interactedwith in recent time, but where the other third-party wants to keep theiruser profiles up to date.

Referring to FIG. 35, in embodiments the present invention may providefor a computer program product embodied in a computer readable mediumthat, when executing on one or more computers, provides improved tasteand preference profiling through the use of a computer facility 3502 byperforming the steps of: (1) creating a first taste and preferenceprofile of a user 3512 through the user's interactions with a firstthird-party website 3514 through a first preference learning third-partyAPI 3504; (2) collecting additional user interaction information througha second third-party website 3514 through a second third-party API 3508;and (3) combining the additional user interaction information with thetaste and preference profile to improve the taste and preference profile3510. In embodiments, creating a first taste and preference profile maybe through the ascertaining of user preferences through the use ofnatural language processing. The computing facility may be a machinelearning facility. The API may enable the collection of at least one ofcost information, product information, personal information, and topicalinformation.

Referring to FIG. 36, in embodiments, graph constructs 3620 may bedeveloped and/or utilized by a web-based advice facility 3602, such asto aid in providing recommendations to users 3608 through a dialog withthe user 3608 across the Internet 3604, with a minimized amount ofdialog with a user, to provide recommendations to users where the graphconstructs augment the process that leads to the recommendation, wherethe use of the graph construct eliminates the need to carry on a dialogwith the user to form recommendations to users, and the like. Inembodiments, graph constructs may be developed through information fromthird-party sites 3610. In embodiments, there may be a plurality oftypes of nodes 3612 in the graph, such as people, entities, tags, andthe like. For instance, people may be users of websites, applications,mobile devices, shoppers in a store, anonymous web browsers representedpurely by a unique cookie id, and the like. Entities may be things thatpeople like, dislike, buy, search for, research, and the like. Tags maybe short textual descriptions of entities, people, and the like. Inembodiments, nodes in the graph may be connected by a plurality of typesof edges 3614, such as for preference data, tagging data, and the like.For instance, entities may be connected to people nodes by preferenceedges that express the degree to which a person likes or dislikes thatentity. Tags may be connected to people and entity nodes by whetherthose people or entities are tagged with those tags.

In embodiments, graph data may be explicitly given by users (e.g. user‘A’ says they like thing ‘B’), crawled from publicly available websites, provided by third-party sources, and the like. Once data isreceived, the system may attempt to “alias” it to existing data in thesystem. For example, if the data tells the system that user ‘A’ likesrestaurant ‘B’, then the system attempts to identify what, if anything,is already known about restaurant ‘B’ through things like matchingnames, addresses, phone numbers, and other information. This may allowthe system to aggregate data, such as training data, from multiplesources all against the same entity representing restaurant ‘B’. Thesystem may perform aliasing against users. For example, user jsmith99might be the same user as john_smith on two different websites. Thesystem may use similarity of usernames, email addresses, pictures, fullfirst and last names, geographic location, and the like to correlateusers across different web sites and identity systems.

In embodiments, nodes in the graph may have a “taste profile”, such aswith a numerical quantity. A person may be predicted to like or dislikean entity or tag based on their taste profiles. Similarly, two peoplemay be predicted to be similar or dissimilar based on their tasteprofiles. Entities may also be compared to see how similar they are toeach other using their taste profiles. The system's graph may initiallyhave taste profiles assigned to some nodes and then propagate thosetaste profiles to the nodes that don't have taste profiles. Thispropagation may be an iterative process that “flows” taste profiles fromnodes that have profiles into nodes that do not have profiles.Alternatively, the iteration may update the profile of nodes thatalready have a profile based on neighboring nodes' profiles. New datamay be incorporated into the graph by adding new nodes or edges and thenupdating the new or changed node purely using neighboring nodes'profiles. Alternatively, the system may run multiple iterations ofupdates across the entire graph.

Many different kinds of data may be fit into being viewed as a “like” or“dislike”. For example, viewing a web page can be represented in thegraph as an edge with a weak connection between the person viewing theweb page and an entity representing the web page. Someone buying a bookcan be represented by an edge making a strong connection between theperson buying the book and the book itself. Someone answering a questionthat has three mutually exclusive answers can be represented as an edgebetween the person and an entity representing the answer they gave aswell as two negative edges to the two answers the user did not give.

In embodiments, methods and systems may provide for recommendations tousers based on the degree to which a recommendation may be new,interesting, and the like, which will herein be referred to as‘interestingness’. In embodiments, interestingness may be a combinationof being an interesting subject, topic, product, and the like, as wellas how new or revived the idea is. In an illustrative example, the usermay live in the U.S. and be interested in cooking Italian food, and sothe system may provide cooking recommendations to the user. In thisinstance, recommending a ‘new’ cheese flavor to try as Parmesan Cheesemay have a low interestingness, because the use of Parmesan Cheese inthe U.S. may not be new at all, and even be rather over used.Alternatively, Pecorino cheese is an Italian cheese made from sheep'smilk, and can be used instead of Parmesan cheese on pasta dishes and issometimes preferable if a sharper taste is desired. As such, arecommendation to substitute Parmesan with Pecorino may be considered tohave a high interestingness, at least in a relative sense to that ofParmesan. Interestingness may be determined relative to what is standardor typical, relative to a past recommendation, relative to a novelfactor, and the like. In this instance, the interestingness of Pecorinomay be rated high relative to the interestingness of Parmesan.

In embodiments, interestingness may be correlated to a known tasteprofile and fresh to the user, where fresh may mean new (such as new tothe world), absent from a user's own past experience (such as byreference to a known history of the user), and the like. Alternately,‘fresh’ may not necessarily be new to the world or to the user, but be anew fact or story associated with something that makes it interestinganew. For example, “Chipotle on 21st street” may not be new orinteresting, but if someone provides a recommendation to “Get a burritoat Chipotle where Oprah gets her burritos” then it's interesting. Thesystem may encourage this by requiring users to write a reason as to whythey are recommending something. Interestingness may be determined asrelated to the ratio of users rating the item, to the item being “saved”by users. For instance, if there is a bookmark, save for later, add towish list, and the like functionality (e.g. a star ratingfunctionality), it may be seen that there is a correlation betweenhighly rated items that are ‘low saved’ items as not being interesting.In this instance, it could be that everyone knows about the items, sothey can rate them, but they're not worth saving for later, thus notinteresting. Alternately, an item that is highly rated and often savedby users may be considered interesting, because the item is both highlyregarded (i.e. highly rated) and worth saving for further consideration.Interestingness may be an acceleration of social activity. For example,there may be a restaurant that has been around forever, and that theuser knows about, but which on a sunny weekend suddenly starts getting alot more foursquare.com check-ins. This may be a sign there's somethinginteresting going on there now. In embodiments, the advice facility mayalso determine that something is new or new to the user by looking atrelease dates of books, movies, albums, products, and the like, and takethe earliest date the item is found on the Internet to determine itsinterestingness; look at the date of the first review written forsomething on the web; look at events such as movie releases, concerts,author talks, and the like, that may be considered inherently new; andthe like. The advice facility may also ask the user to rate things theyalready know about, where the system may assume that items user'shaven't rated are new to them. The advice facility may have a ‘save’feature to encourage users to use when they don't know about somethingyet but want to check it out.

Thus it will be understood that ‘interestingness’ as that term is usedherein may include (or more concretely, be quantitatively evaluatedaccording to) relevance in the conventional sense, particularly as itrelates to the relationship between a user's profile (or taste profile)and new content. A wide variety of analytical, mathematical, rule-based,and/or heuristic techniques are known for evaluating relevance, any ofwhich may be usefully adapted to determining relevance (and moregenerally interestingness) as contemplated herein. However,interestingness additionally includes dynamic relationships between auser and content based on, e.g., time, location, user history, and soon.

Time, for example, may be important simply as a measure of newness, suchas where a current statistic is more interesting than an oldermeasurement of the same statistic. Conversely, where a user expressesinterest in a particular point in time or period in history, olderstatistics, facts, opinions, and the like having an explicit time (asdetermined by metadata, content, chronology, or the like) may be moreinteresting. Newness—that is, a measure of how recent an item is—may beparticularly important to interestingness where there are numerousdiverging items of information on a topic and there is a measurableincrease in the current popularity of or interest in particular ones ofthe diverging items. This type of popularity may be measured in numerousways such as passive measurements of blogging activity, newly indexedweb content, or any other Internet-based measurement of user interest,as well as active measurements of hits, traffic, or other activity atweb servers, as well as group or individual monitoring of clientactivity. Time may also be important to interestingness of an item inother contexts, such as where time is explicit or implicit in a userinquiry, e.g., things to do this weekend, movies showing this evening,etc.

Location may also significantly impact interestingness. This may includesimple geographic proximity using any suitable location-awaretechnologies, and may incorporate other aspects of a user profile suchas an interest in particular venues (e.g., food, art, entertainment) ora current activity associated with a user. However, it will beunderstood that this may also include location-related items such asinferences about the convenience of adjacent locations through varioustransportation alternatives available to the user (e.g., a car, publictransportation, etc.), as well as a user's available budget forimmediate or extended travel planning. In addition, the context of alocation and its corresponding interestingness may depend on otherdynamic location attributes such as the location of friends within asocial network, and the proximity to or distance from geographicconcentrations of the same.

User history may also be used to parameterize interestingness. Forexample, where a new item is responsive to a user inquiry or wellmatched to a user profile, but highly distinct from previous contentobtained by a user, this distinctiveness may make the item morequantitatively interesting even if the calculated relevance is equal toor less than relevance of other results. Thus in one aspect,interestingness may depend concurrently on measures of similarity (orrelevance, or the like) and dissimilarity, or more specifically,characteristics that make an item dissimilar to previous content in auser's history. Alternatively, an item of information may rank poorly ona general measure of relevance that is de-emphasized based on otheraspects of the user's current context. Thus interestingness may providea measure of relevance to a user based on any suitable similarity ormatching metric that is further augmented by a newness to a user, asexplicitly measured through a dissimilarity to information in a user'shistory, or any objective basis for adapting relevance scoring based onthe user's context. In one aspect, interestingness may be objectivelymeasured as relevance based on a user's profile along with dissimilarityto a user's history and one or more aspects of a user's current contextsuch as time or location. As measured in this manner, many objectivelyhighly relevant items may not be particularly interesting to a user,while marginally relevant items may be highly interesting.

Referring to FIG. 37, recommendations may be provided through arecommendation facility 3704 as part of a web-based advice facility3702. In embodiments, the recommendation facility may utilizeinterestingness filtering 3708 in the process of generatingrecommendations to a user 3722. Recommendations sources may includefriends 3718, similar users 3714, influential people 3720, sourcewebsites 3712, and the like. Recommendations may be provided to the userthrough the Internet 3710, through a telecommunications facility 3724(e.g. cell phone network), and the like.

In embodiments, the determination of interestingness may be related to‘social activity’ of other individuals (e.g. friends, famous people, anauthoritative person), the ‘born on’ date of a product, place, event(e.g. the opening of a restaurant, the release of a movie, a newproduct), and the like. The social activity of other individuals may berelated to individuals influential to the user, such as friends highlyrating a topic, friends with similar tastes highly rating a topic,non-friends who are influential highly rating a topic, non-friends whohave similar tastes in this topic highly rating it, and the like. Theseother individuals may fall into different categories, such as friends;people the user doesn't necessarily know but whom have similar tastes inthis topic; people who don't necessarily have the same tastes as theuser but who are famous, prolific, well-known; critics in this topic(e.g. movie reviewers); and the like. The system may also note which ofthe user's friends have similar tastes to them in this specific topic.For example, if Ted has similar restaurant tastes to the user, but Alicedoes not, then a restaurant may be interesting to the user if Ted likesit but not necessarily interesting if Alice likes it. Other reasons thatsomething may be considered interesting for the user are if a friend hassaved the recommendation for later, if friends are commenting anddiscussing the recommendation actively, and the like.

In embodiments, recommendations may be provided to users through email,social networks, third-party sites, when the user requests, as a datafeed, as a push service, on a periodic basis, in association with asearch topic, related to a current geographic location, to a homecomputer, to a mobile computer, to a mobile communications facility(e.g. cell phone, smart phone, PDA), and the like. For example, the usermay be provided interestingness recommendations to their mobile phonebased on their current geographic location, such as productrecommendations to stores in the area (e.g. products on sale, newproducts, products that are difficult to get), places to see,restaurants to try, and the like, where the recommendations are based oninterestingness. In this way, the user doesn't just receiverecommendations, but rather a more interesting set of recommendations,which may increase the chances that the user will be interested in therecommendation, such as in a ‘discovery’ of a new idea, place, product,and the like. And when the discovery is associated with a particulargenre, becomes a ‘local discovery’, a ‘restaurant discovery’, a‘technology discovery’, a ‘cooking discovery’, and the like.

In embodiments, the present invention may provide ‘local discovery’ to auser, where local discovery may include providing new and interestingthings to the user instead of relying on the user typing something intoa search box or otherwise “pulling” search results to them. This may beespecially useful for mobile devices where typing is more difficult,such as when a device input is input constrained (e.g. small keyboard,small display, the user being mobile (walking, driving), and the like).Although the description of local discovery herein is provided primarilywith respect to a mobile device application, one skilled in the art willappreciate that it may be implemented on any computing facility, such asa laptop, desktop, navigation device, or the like. Local discoveryfunctionality may also be available through a web interface, throughemails of “new stuff” for the user, through Twitter, through posting toblogging platforms (e.g. Wordpress, Tumblr, etc), and the like. Further,local discovery content may be provided to a user upon request,transmitted to the user (e.g. email) to push new interesting things to auser (e.g. weekly), and the like.

In embodiments, a mobile device local discovery application may show theuser places nearby, such as that they've rated in the application in thepast, that their friends have rated, that people with similar taste asthe user have rated, that authoritative sources have rated, that famouspeople have rated, and the like, where ‘rating’ may be an inferredrecommendation from behaviors (e.g. online or off-line) of the person.In embodiments, the places that get shown may be restaurants, bars,boutiques, hotels, and the like. There may also be a navigationalelement to let the user filter down to narrower lists, such as forexample, “Italian Restaurants” that are nearby or at a specifiedlocation, such as recommended by others.

Besides showing places, local discovery may also show the user items tobuy, events to go to, things to look at (such as if they have some timeto kill), and the like. For example, local discovery might provide alist of recommended books the user might want to read and optionallyshow where to buy them locally. Local discovery might select the items,provide recommendations, and the like, based on machine learning, suchas what the user's friends have liked recently, what people with similartastes in books have liked recently, what popular/prominent critics haveliked recently, and the like, or just what is popular overall or popularnear the user. Similarly, this also applies to other kinds of products,events to go to, and the like.

In embodiments, local discovery may find people with similar tastes ineach area (restaurants, books, etc) and then let the user follow thethings they rate. When using the mobile application, local discovery mayuse the user's location to filter down lists of things people similar tothe user like near by. Local discovery may determine whether anotherperson has similar taste as the user through machine learning, askingboth to rate various places and things, asking both to answer questionsto gauge similarity, and the like. Local discovery, such as though theadvice facility, may then try to validate similarity between the userand another person, such as based on liking obscure things in common,disliking popular things in common, showing written reviews that theother person has written, describing the other person's traits(demographics, location, etc), showing how many other people follow theother user, and the like.

In embodiments, instead of producing a list of recommended places,things, or events for a given area, local discovery may also produce a“discovery” feed of interesting stuff for the user, such as with a highinterestingness rating. This may mean that instead of seeing the sameten restaurants recommended every time the user looks near their office,they may see a few different results each day show up. Ideally these newrestaurants may be showing up based on the user's friends or people withsimilar tastes liking some new place near you, but it may also be apartially editorial process where staff members associated with theimplementation of a local discovery application are constantly findingnew places and sending them out to the user base.

In embodiments, the user may also save things to a “wish list”, “to dolist”, and the like, for using later. This saved list of products,places, events, and the like, may then also be used to alert the userabout deals, availability, new reviews, and the like, about thoseproducts, places and events so as to only alert the user about stuffthey're interested in. For example, it might be annoying if the user'sphone vibrated to tell them some shoes were available at a store theywere walking by unless they had previously indicated they wanted thoseshoes on their “wishlist”.

In embodiments, the user interface for a local discovery application maybe a map, a textual list, a “cover flow” like interface for flippingthrough (such as in Apple Computer's implementation of cover flow), andthe like. The interface may also send alerts to the user when a friendor someone with similar taste likes something nearby, likes somethingthat the user has saved to their wish list, and the like.

In embodiments, a local discovery application may be associated with theweather, where the application may in part determine recommendationsbased on actual or predicted weather in the area the person wants arecommendation in. For example, the application may recommend placeswith great outdoor seating when the weather is warm and not raining,recommend things to do outdoors when the weather is good, provide moreweight to recommendations for going to a museum when the weather is bad,and the like.

In embodiments, a local discovery application may be associated with thetime of day, such as taking the hours of operation and the distance tothe place into account when making recommendations. For example, if theperson wants a place to eat now, the system may not recommend a placethat is closed or about to close. Similarly, the system wouldn'trecommend things to do that have sold out already, that will not bereachable in time using estimated transportation time to reach them, andthe like.

In embodiments, a local discovery application may be associated withtaste, such as using a person's taste when making recommendationsinstead of just showing what's popular among users nearby. Inembodiments, taste may be inferred through the things the user likes,the people they follow on social networks, and the like, such asdescribed herein.

In embodiments, a local discovery application may be associated withlocation, such as using the person's location when makingrecommendations, use their location when offering the user a list oftopics they can get a recommendation in, and the like. For example, ifthere are no hair salons near by, the system would not offer the userthe choice of getting recommendations about hair salons. Similarly, ifthere are no Mexican restaurants or video game stores, the system wouldnot offer the choice of recommendations in those topics. If the user'slocation is inside a store, the system wouldn't offer recommendationsabout topics the store is out of or does not sell.

In embodiments, a local discovery application may be associated withsocial activity, such as showing recommendations that have received someamount of activity from friends, from respected authorities in thetopic, from people with similar tastes as the user, and the like.

In embodiments, a local discovery application may be associated withrecommendations that are interesting in that they are new, newlypopular, have received social activity, have an indication that they arenot new but also not popular enough that the user is likely to alreadyknow about them, and the like, such has described herein in terms ofinterestingness.

In embodiments, a local discovery application may be associated withwhat items a user saves on their mobile device, such as when a personusing a mobile application may “save for later” interestingrecommendations as well as things they see in stores or while outwalking around. For example, if they see a book they like in a bookstore, they may scan the bar code and save it for later. If they see arestaurant they like they may take a picture and capture their location,such as via GPS, and save it for later. Saved content may then be thebasis of recommendations used in a mobile application later on, or thecontent may be sent to the user through other channels, such as a weeklyemail reminder, through a web application, and the like. Saving may alsobe used by the system as a social indicator, such as to indicate thatsomething is interesting content for other users.

In embodiments, a local discovery application may be associated withwhat items a user saves on the Internet. This may be similar to savingon a mobile device itself, where the user may save content they find onthe web and then use it later through their mobile application. Forinstance, if the user sees a book review and saves it for later they maythen be reminded about it later when they use their mobile device tolook for book recommendations. Similarly they may save a restaurant orthing to do and later be reminded about it on their mobile device.

In embodiments, a local discovery application may be associated withdisplaying topics to offer recommendations in, such as displaying a gridof pictures when the mobile application first starts representing thetopics that recommendations are available in. The choice may be based onthe user's location, their historic use of the application, and thelike, where the application predicts what the user is interested in. Forexample, there might be pictures of restaurants, iPhone apps and videogames among others when the application starts based on their beingrecommended restaurants near the user and the application's belief thatthe user is interested in iPhone apps and video games. The actualpictures may be picked based on the application's knowledge of the user.For example, the picture for the restaurants topic may be a picture of arestaurant nearby that the application predicts the user may like. Thesize and sorting of the pictures may be based on how interested theapplication predicts the user will be in that topic so that the user'smost frequently used topics are at the top of the list and shown withthe biggest picture, such as shown in FIG. 38. The user may then selectone of the recommendations, and be linked to more detailed informationassociated with the recommendation, such as shown in FIG. 39.

In embodiments, recommendations may be displayed as the images with anordering as a function of interest to the user, such as in an irregulargrid where the left-right top-to-bottom ordering is based on how muchthe system thinks the user will be interested in each recommendation oreach topic. For example, as shown in FIG. 40 the system has predictedthat the user is more interested in restaurants than coffee shops and sothe restaurants tile is listed before the coffee shop tile. Also, thecontent of each tile is a recommendation in that topic that the user maylike. So the restaurant tile shows a restaurant the user may like thatis near them, the movie tile shows a movie the music may like, etc.

In embodiments, a local discovery application may be associated withdetermining a radius to get recommendations from, such as theapplication has to pick a radius of how far from the user's locationrecommendations will be returned in. The radius may be selected by theuser, based on the population density of the area around the user, andthe like. For example, in NYC the radius might be 0.025 miles while inrural SC the radius might be 60 miles.

In embodiments, a local discovery application may be associated withdetermining which people to show reviews from, such as whenrecommendations are shown in the application they may be accompanied byreviews, ratings or other recommendations from people. The applicationmay choose which people to show based on whether they have similar tasteas the person using the application, whether they are friends of theuser, whether they are authoritative critics, and the like, such asshown in FIG. 41.

Referring to FIG. 42, methods and systems may provide a recommendationto a user through a computer-based advice facility 4202, comprisingcollecting topical information, wherein the collected topicalinformation includes an aspect related to the extent to which a topic isinteresting, or an interestingness aspect; filtering the collectedtopical information based on the interestingness aspect 4204;determining an interestingness rating from the collected topicalinformation, wherein the determining is through the computer-basedadvice facility 4208; and providing a user with the recommendationrelated to the topical information based on the interestingness rating4210. In embodiments, the interestingness aspect may be derived at leastin part from social activity of another individual that indicates arecommendation for a topic. The other individual may be a friend, afamous person, an authoritative person, or and the like. The otherindividual may have similar tastes to the user, either in general, orwith respect to a particular category or type of interest. The socialactivity may be saving a recommendation. The social activity may be atleast one of commenting and discussing a recommendation actively. Thesocial activity may be collected from activity of the other individualwith respect to sources on the Internet, such as social networkingactivity. The interestingness aspect of the topical information may alsobe determined based upon an indication that the topical information hassome aspect of freshness or newness. The newness may be an indicationthat the topical information is new topical information over apredetermined period of time. The newness may be an indication that thetopical information is newly popular. Status as newly popular may bedetermined from an activity level on the web. Newness may be general,such as the emergence of a newly popular topic within a social network,or it may be particular to a user, such as when an older topic is firstexposed to a user, making it new to the user, if not to the socialnetwork as a whole. The interestingness aspect of the topicalinformation may be based at least in part from at least one of a review,a recommendation, a blog entry, a tweet, an authoritative source, a newssource, an e-publication, a purchase, a view, a time viewed or and thelike. Without limitation, the interestingness aspect may be based ontime data. The time data may be a release date, such as a movie, aproduct, and the like. The time data may be an event opening, such as arestaurant opening, a cultural event opening, and the like. Theinterestingness aspect may be frequency data, such as relating to howfrequently the topical information is referenced in online sources. Theinterestingness aspect may be related to a user interaction with acomputer device. The user interactions may be interpreted by a machinelearning facility as user behavior that indicates a preference level forthe topical information by the user. The user interaction may beselection of a web link. The user interaction may be at least one oftapping, touching, and clicking on the computer device screen. Thecomputer-based advice facility may include a machine-learning facility.The computer-based advice facility may include a recommendationfacility. The filtering may be collaborative filing. Recommendations maybe sent to a user's mobile communications facility to providerecommendations in the user's current geographic area. There may be agraphical user interface on the mobile communications facility thatprovides the user with the ability to refine provided recommendations tothe user. The recommendations may show the user at least one of items tobuy, events to go to, things to see, and the like. The recommendationmay be related to a local store. The recommendation may be related to alocal restaurant. The recommendation may be related to a local bar. Therecommendation may be related to entertainment. Recommendations may befurther filtered to the user based on interestingness specific to thegeographic area. A recommendation feed may be sent to the user for thecurrent geographic area the user is located in. Recommendations may onlybe sent to the user that meet a threshold in confidence and in how muchthe system predicts the user will like the recommendation. The thresholdin confidence may be related to the interestingness rating. Thethreshold in confidence may be determined by a machine learning facilitybased on past behavior of the user as related to previousrecommendations provided by the system. The user may be able to saverecommendations to storage on the mobile communications facility. Theuser may be able to save recommendations to storage with thecomputer-based advice facility.

Referring to FIG. 43, methods and systems may provide a geographicallylocalized recommendation to a user through a computer-based advicefacility 4302, comprising collecting a recommendation from an Internetsource, wherein the recommendation is determined to have aninterestingness aspect. The recommendation may further be determinedbased on a geographic location aspect 4304. Further options may includecomparing the collected recommendation to a derived user taste and theuser's current geographic location 4308, determining at least onerecommendation for the user based on processing on the comparison 4310,and delivering at least one recommendation to a user's mobilecommunications device, wherein the user is enabled to at least one ofview, save, and share the recommendation, such as via an application atleast in part resident on the computer-based advice facility 4312.

In embodiments, the computer-based advice facility may be a mobilecommunications device. The mobile communications device may be asmart-phone. The viewing may include providing source information fromthe Internet source. The source information may include the originalrecommendation, a rating, an image associated with the Internet source,or the like. The image may be a photo of an individual who provided therecommendation, or another indication, such as an icon, representativeof such an individual. The source information may include a visualindictor of an extent of similarity to the tastes of at least oneindividual who made the recommendation. The indication may bequalitative (e.g., “this individual has tastes highly similar to yours”)or quantitative, such as expressing a metric that measures relativesimilarity (e.g., “you share 10 interests out of 20 categories with thisindividual”). The source information may include a visual indicator ofan extent of similarity to the tastes of more than individual who made arecommendation. The visual indicator may indicate a sorting ofindividuals based on the extent of taste similarity. Individuals may belisted in decreasing order of similarity.

In embodiments, the interestingness aspect may be topical informationthat is new, such as determined by a date of emergence of theinformation within a domain, such as an Internet domain, a collection ofInternet news sources, an enterprise network, a social network, or theInternet as a whole.

The interestingness aspect may be topical information that is deemed bythe advice facility to be new to the user, such as by comparison to pastcontent reviewed by the user, accessed by the user, or the like, asreflected by a user's browsing history, by tracking the user'sactivities on one or more devices, or the like.

In embodiments the interestingness aspect may be topical informationthat has a new aspect to an existing topic, such as an update to a newsitem in which the user has shown interest in the past, such as reflectedby user feedback or by a user's activities, such as access to the item,time spent reviewing it, or the like.

The interestingness aspect may be determined as related to the ratio ofusers rating the item, or to the item being saved by users.

In embodiments the interestingness aspect may be determined by anacceleration of social activity associated with the topical information.

In embodiments the interestingness aspect may be determined based oninformation having more than one aspect, such as the information beingdetermined to be similar to a user's profile (e.g., similar to items inwhich the user has shown past interest, matching a category of theuser's interest, showing relevance or interest to other users who havesimilar tastes, or the like) while at the same time being dissimilar toa user's history (i.e., being new to this user in one of the ways notedabove). Thus, for example, a user who has expressed a past in aparticular celebrity might be expected to have very high interest in abreaking news item with respect to that celebrity.

In embodiments, the interestingness aspect may be further determinedbased on a user's current location and a temporal factor, where thetemporal factor is based on topical information that is new asdetermined by one of the factors noted in this disclosure, such as beingnew to a domain (up to an including the entire Internet, but optionallybeing based on being new with respect to a domain that has a link to theuser's current location), based on topical information that is deemed bythe advice facility to be new to the user, based on topical informationthat has a new aspect to an existing topic, determined as related to theratio of users rating the item, based on the item being saved by users,determined by an acceleration of social activity associated with thetopical information, and the like. The geographical aspect may be ageographical location associated with the topical information, where thegeographical location may be the location of an event, the location of astore, the location of restaurant, the location of point-of-interest, atleast one product location, and the like.

In various embodiments the derived user taste may be based on a rating,where the rating may be provided by the user, friends of the user,people with similar taste as the user, an authoritative source, a famousperson, inferred from user behavior, on machine learning with respect touser online behavior, and the like. The user behavior may be onlinebehavior, including buying behaviors, browsing behavior, socialnetworking behavior, location-based behaviors, and the like. Therecommendation may be items to buy, places to visit, events to attend,places to eat, and the like. The recommendation may be based on one ofratings and recommendations of at least one other user with similartastes to the user from the current geographic location of the user. Thesimilar tastes may be determined by machine learning through at leastone of ratings from the other user and online behavior of the otheruser. The recommendation may be provided as part of a feed of localdiscovery recommendations. The recommendation may be saving of arecommendation saving to a list, where the list may be a wish list,to-do list, an events list, a deals list, and the like. A savedrecommendation may be shown to the user through the local discoveryapplication when the geographic location aspect of the savedrecommendation matches the current location of the user. Arecommendation may be forwarded to a user based on a recommendation fromat least one other user, where the advice facility determines anapplicability radius around the user's current location for use of theother user's recommendation. The application may be a local discoveryapplication, where the local discovery application correlates at leastone of new and saved recommendations with the weather, at least one ofnew and saved recommendations with the time of day, at least one of newand saved recommendations with the user's social activity, and the like.The local discovery application may display images based on how much theadvice facility thinks the user will be interested in at least one ofeach recommendation and each topic. The displayed images may bedisplayed in an irregular grid where the left-right top-to-bottomordering may be based on how much the advice facility thinks the userwill be interested in at least one of each recommendation an each topic.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present invention may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. The processor may be part of a server, client, networkinfrastructure, mobile computing platform, stationary computingplatform, or other computing platform. A processor may be any kind ofcomputational or processing device capable of executing programinstructions, codes, binary instructions and the like. The processor maybe or include a signal processor, digital processor, embedded processor,microprocessor or any variant such as a co-processor (math co-processor,graphic co-processor, communication co-processor and the like) and thelike that may directly or indirectly facilitate execution of programcode or program instructions stored thereon. In addition, the processormay enable execution of multiple programs, threads, and codes. Thethreads may be executed simultaneously to enhance the performance of theprocessor and to facilitate simultaneous operations of the application.By way of implementation, methods, program codes, program instructionsand the like described herein may be implemented in one or more thread.The thread may spawn other threads that may have assigned prioritiesassociated with them; the processor may execute these threads based onpriority or any other order based on instructions provided in theprogram code. The processor may include memory that stores methods,codes, instructions and programs as described herein and elsewhere. Theprocessor may access a storage medium through an interface that maystore methods, codes, and instructions as described herein andelsewhere. The storage medium associated with the processor for storingmethods, programs, codes, program instructions or other type ofinstructions capable of being executed by the computing or processingdevice may include but may not be limited to one or more of a CD-ROM,DVD, memory, hard disk, flash drive, RAM, ROM, cache and the like.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server and other variants such as secondaryserver, host server, distributed server and the like. The server mayinclude one or more of memories, processors, computer readable media,storage media, ports (physical and virtual), communication devices, andinterfaces capable of accessing other servers, clients, machines, anddevices through a wired or a wireless medium, and the like. The methods,programs or codes as described herein and elsewhere may be executed bythe server. In addition, other devices required for execution of methodsas described in this application may be considered as a part of theinfrastructure associated with the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe invention. In addition, any of the devices attached to the serverthrough an interface may include at least one storage medium capable ofstoring methods, programs, code and/or instructions. A centralrepository may provide program instructions to be executed on differentdevices. In this implementation, the remote repository may act as astorage medium for program code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient and other variants such as secondary client, host client,distributed client and the like. The client may include one or more ofmemories, processors, computer readable media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other clients, servers, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs or codes asdescribed herein and elsewhere may be executed by the client. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe invention. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements.

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (FDMA) network or code division multiple access (CDMA) network.The cellular network may include mobile devices, cell sites, basestations, repeaters, antennas, towers, and the like. The cell networkmay be a GSM, GPRS, 3G, EVDO, mesh, or other networks types.

The methods, programs codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on a peer topeer network, mesh network, or other communications network. The programcode may be stored on the storage medium associated with the server andexecuted by a computing device embedded within the server. The basestation may include a computing device and a storage medium. The storagedevice may store program codes and instructions executed by thecomputing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable media that may include: computercomponents, devices, and recording media that retain digital data usedfor computing for some interval of time; semiconductor storage known asrandom access memory (RAM); mass storage typically for more permanentstorage, such as optical discs, forms of magnetic storage like harddisks, tapes, drums, cards and other types; processor registers, cachememory, volatile memory, non-volatile memory; optical storage such asCD, DVD; removable media such as flash memory (e.g. USB sticks or keys),floppy disks, magnetic tape, paper tape, punch cards, standalone RAMdisks, Zip drives, removable mass storage, off-line, and the like; othercomputer memory such as dynamic memory, static memory, read/writestorage, mutable storage, read only, random access, sequential access,location addressable, file addressable, content addressable, networkattached storage, storage area network, bar codes, magnetic ink, and thelike.

The methods and systems described herein may transform physical and/oror intangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable media having aprocessor capable of executing program instructions stored thereon as amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations may be within thescope of the present disclosure. Examples of such machines may include,but may not be limited to, personal digital assistants, laptops,personal computers, mobile phones, other handheld computing devices,medical equipment, wired or wireless communication devices, transducers,chips, calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipments, servers, routers and the like.Furthermore, the elements depicted in the flow chart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried, and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps thereof, may berealized in hardware, software or any combination of hardware andsoftware suitable for a particular application. The hardware may includea general purpose computer and/or dedicated computing device or specificcomputing device or particular aspect or component of a specificcomputing device. The processes may be realized in one or moremicroprocessors, microcontrollers, embedded microcontrollers,programmable digital signal processors or other programmable device,along with internal and/or external memory. The processes may also, orinstead, be embodied in an application specific integrated circuit, aprogrammable gate array, programmable array logic, or any other deviceor combination of devices that may be configured to process electronicsignals. It will further be appreciated that one or more of theprocesses may be realized as a computer executable code capable of beingexecuted on a machine readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, each method described above and combinationsthereof may be embodied in computer executable code that, when executingon one or more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the invention has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present invention isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

All documents referenced herein are hereby incorporated by reference.

What is claimed is:
 1. A method of providing a recommendation to a userthrough a computer-based advice facility, comprising: collecting topicalinformation, wherein the collected topical information includes aninterestingness aspect, the interestingness aspect being based on thesimilarity to a user's taste profile and based on newness of the topicalinformation, wherein the interestingness aspect of the topicalinformation is an indication that the topical in has some aspect ofnewness; filtering the collected topical information based on theinterestingness aspect; determining an interestingness rating from thecollected topical information, wherein the determining is through thecomputer-based advice facility; and providing a user with therecommendation related to the topical information based on theinterestingness rating.
 2. The method of claim 1, wherein theinterestingness aspect is derived from social activity of anotherindividual that indicates a recommendation for a topic.
 3. The method ofclaim 2, the other individual is a friend.
 4. The method of claim 2,wherein the other individual is a famous person.
 5. The method of claim2, wherein the other individual is an authoritative person.
 6. Themethod of claim 2, wherein the other individual has similar tastes tothe user.
 7. The method of claim 2, wherein the social activity issaving a recommendation.
 8. The method of claim wherein the socialactivity is at least one of commenting and discussing a recommendationactively.
 9. The method of claim 2, wherein the social activity iscollected from sources on the Internet.
 10. The method of claim 1,wherein the newness is an indication that the topical information is newtopical information over a predetermined period of time.
 11. The methodof claim 1, wherein the newness is an indication that the topicalinformation is newly popular.
 12. The method of claim 11, wherein newlypopular is determined from an activity level on the web.
 13. The methodof claim 1, wherein the interestingness aspect of the topicalinformation is from at least one of a review, recommendation, blogentry, tweet, authoritative source, news source, and e-publication. 14.The method of claim 1, wherein the interestingness aspect is time data.15. The method of claim 14, wherein the time data is a release date. 16.The method of claim 15, wherein the release date is a release date of amovie.
 17. The method of claim 15, wherein the release date is a releasedate of a product.
 18. The method of claim 14 wherein the time data isan event opening.
 19. The method of claim 18, wherein the opening is arestaurant opening.
 20. The method of claim 18, wherein the opening is acultural event opening.
 21. The method of claim 1, wherein theinterestingness aspect is how frequently the topical information isreferenced in online sources.
 22. The method of claim 1, wherein theinterestingness aspect is related to a user interaction with a computerdevice.
 23. The method of claim 22, wherein the user interactions areinterpreted by the machine learning facility as user behavior thatindicates a preference level for the topical information by the user.24. The method of claim 22, wherein the user interaction is selection ofa web link.
 25. The method of claim 22, wherein the user interaction isat least one of tapping, touching, and clicking on the computer devicescreen.
 26. The method of claim 1, wherein the computer-based advicefacility includes a machine-learning facility.
 27. The method of claim 1wherein the computer-based advice facility includes a recommendationfacility.
 28. The method of claim 1, wherein the filtering iscollaborative filing.
 29. The method of claim 1, wherein recommendationsare sent to a user's mobile communications facility to providerecommendations in the user's current geographic area.
 30. The method ofclaim 29, wherein there is a graphical user interface on the mobilecommunications facility that provides the user with the ability torefine provided recommendations to the user.
 31. The method of claim 29,wherein the recommendations show the user at least one of items to buy,events to go to, and things to see.
 32. The method of claim 29, whereinthe recommendation is related to a local store.
 33. The method of claim29, wherein the recommendation is related to a local restaurant.
 34. Themethod of claim 29, wherein the recommendation is related to a localbar.
 35. The method of claim 29, wherein the recommendation is relatedto entertainment.
 36. The method of claim 29, wherein recommendationsare further filtered to the user based on interestingness specific tothe geographic area.
 37. The method of claim 29, wherein arecommendations feed is sent to the user for the current geographic areathe user is located In.
 38. The method of claim 29, whereinrecommendations are only sent to the user that meet a threshold inconfidence and in how much the system predicts the user will like therecommendation.
 39. The method of claim 38, wherein the threshold inconfidence is related to the interestingness rating.
 40. The method ofclaim 38, wherein the threshold in confidence is determined by a machinelearning facility based on past behavior of the user as related toprevious recommendations provided by the system.
 41. The method of claim29, wherein the user is able to save recommendations to at, least one ofstorage on the mobile communications facility and storage with thecomputer-based advice facility.
 42. A non-transitory machine-readablestorage medium comprising instructions that, when executed by one ormore processors of a machine, cause the machine to perform operationscomprising: collecting topical information, wherein the collectedtopical information includes an interestingness aspect, theinterestingness aspect being based on the similarity to a user's tasteprofile and based on newness of the topical information, wherein theinterestingness aspect of the topical information is an indication thatthe topical information has some aspect of newness; filtering thecollected topical information based on the interestingness aspect;determining an interestingness rating from the collected topicalinformation, wherein the determining is through the computer -basedadvice facility; and providing a user with the recommendation related tothe topical information based on the interestingness rating.
 43. Asystem comprising: a machine including a memory and at least oneprocessor; and a computer-based advice facility, executable by themachine, configured to: collect topical information, wherein thecollected topical information includes an interestingness aspect, theinterestingness aspect being based on the similarity to a user's tasteprofile and based on newness of the topical information, wherein theinterestingness aspect of the topical information is an indication thatthe topical information has some aspect of newness; filter the collectedtopical information based on the interestingness aspect; determine aninterestingness rating from the collected topical information, whereinthe determining is through the computer-based advice facility; andprovide a user with the recommendation related to the topicalinformation based on the interestingness rating.